The enterprise technology ecosystem is caught in a costly cycle. Over the past two years, millions of dollars have been funneled into generative AI pilots, yet many of these initiatives stall out before ever reaching a live production environment.

When a project fails, the immediate instinct of technical leadership is often to blame the model: The context window was too restrictive, the latency was too high, or the reasoning capabilities simply were not there.

But as data engineers building the scaffolding for these systems, we often see a different reality: The model receives the blame, but the pipeline usually contains the root cause. Production gen AI rarely fails because of model limitations alone. More often, it fails because the enterprise data foundation underneath it is fundamentally unready.

This is what I call the 'Cleanup Trap': The false belief that an organization can pipe fragmented, inconsistent, and ungoverned legacy data into a large language model (LLM) orchestrator and simply “clean it up” or patch it at the retrieval layer.

The mirage of the retrieval layer

In a standard retrieval-augmented generation (RAG) architecture, the retrieval layer is tasked with pulling relevant business context to ground the model’s responses. Because modern frameworks make it simple to stand up a vector database and a basic embedding pipeline, leadership often assumes that the data engineering problem is solved.

It is not.

When an embedding model receives raw, unvalidated data directly from operational silos, the resulting vector space inherits the structural noise, duplicate records, and conflicting states present in the source systems.

If the core data pipeline suffers from silent degradation — schema drift, missing fields, delayed change-data-capture (CDC) synchronization — that degradation cascades directly into the vector store. An AI model cannot accurately synthesize customer intelligence if the data pipeline behind it is serving stale, contradictory profiles across disparate storage layers.

No amount of prompt engineering, semantic reranking, or vector hyperparameter tuning can compensate for a broken ingestion pipeline. If the foundation is compromised, the downstream application will hallucinate, expose unauthorized context, or fail to deliver deterministic value.

Shifting from ad-hoc patching to programmatic guardrails

To break out of the 'Cleanup Trap,' enterprise data teams must stop treating data quality as a post-processing step. They need to treat data readiness for AI with the same rigor they bring to traditional transaction processing.

This requires a deliberate architectural shift toward zero-trust data ingestion, structured validation frameworks, and automated anomaly detection before data ever reaches an AI orchestration layer.

1. Harden the ingestion pipeline

Data quality checks cannot exist as a nightly batch afterthought. If an enterprise AI application relies on real-time data to assist users, validation must happen inline.

Teams should implement explicit schema validation checks at the earliest ingestion point, such as the streaming ingress layer or the bronze landing layer of a medallion architecture. If an upstream operational database mutates a schema without warning, the pipeline should quarantine anomalous payloads rather than allowing corrupted metadata to pollute downstream AI contexts.

2. Use multi-tiered algorithmic validation

Static row-count validation rules are insufficient for AI readiness. True data health requires a multi-tiered approach.

This means pairing structural verification — null checks, type conformance, and schema validation — with statistical profiling to monitor for data drift. Tracking metric deviations across feature distributions helps ensure that historical context remains stable over time.

If a pipeline suddenly processes an unexpected spike in empty string variables or structurally deviant fields, automated alerts should trigger an immediate pause before vector database updates continue.

3. Decouple security and compliancemfrom the model

An LLM should never be the arbiter of data access control. Trying to enforce row-level security or personal data filtering through system prompts is a compliance risk.

Security must be managed within the data infrastructure tier. Enterprise data foundations should enforce strict access controls, tokenization of sensitive identifiers, and rigorous lineage tracing before information is indexed into vector stores or passed into an agent’s context window.

Technical alignment: A pragmatic blueprint

For technology leaders mapping their infrastructure roadmaps, AI readiness requires evaluating data pipelines against a strict operational checklist.

• Can you trace a flawed AI response back to the exact pipeline execution, source record, and transformation step that produced it?

• Does your data lake architecture have a programmatic mechanism to segment and quarantine corrupted or non-compliant data before it reaches production feature stores?

• Are your operational systems and AI-facing vector databases tightly synchronized, or are your agents making automated decisions based on outdated snapshots?

These questions matter because production AI is not just a model deployment problem. It is a data reliability problem.

Building for the production era

The honeymoon phase of gen AI experimentation is ending. Enterprise leaders are demanding measurable, predictable, and secure business outcomes from their AI investments.

If an organization wants to transition from isolated, impressive-looking demos to resilient, production-grade AI systems, it must redirect its focus. Stop looking exclusively at the model tier.

The real competitive differentiator is not only the LLM an organization chooses. It is the engineering discipline, data governance, and pipeline resilience of the infrastructure built to feed it.

In the production era of AI, data engineering is no longer a backend function. It is the control plane for enterprise intelligence.

Naveen Ayalla is a senior data engineer.

Capital One on Thursday released VulnHunter, an open-source, agentic AI security tool that scans source code for exploitable vulnerabilities, maps out how an attacker would reach them, and proposes targeted fixes — all before a single line ships to production. The tool, built internally and now available on GitHub under an Apache 2.0 license, is one of the most ambitious attempts by a major financial institution to turn offensive AI capabilities into a public defensive resource.

At a time when security teams are facing a rising tide of new AI threats, Capital One's decision to open-source the tool reflects an effort, according to CISO Chris Nims, to address "an increasingly brief window before sophisticated, next-generation AI attack capabilities become affordable and accessible to virtually every adversary."

Capital One is not simply releasing another vulnerability scanner. VulnHunter introduces what the company calls an "attacker-first forward analysis" — a workflow in which the tool begins at the points where a real adversary would enter a system, such as APIs, network messages, or file uploads, and reasons forward through the application's logic to determine whether an exploit path actually survives the code's existing defenses. Conventional scanners typically work in reverse, flagging a dangerous-looking code pattern and then searching backward for a hypothetical attacker. That approach, security practitioners widely acknowledge, buries engineering teams under avalanches of false positives.

VulnHunter attacks that problem head-on with a second innovation: a built-in "falsification engine" that tries to disprove its own findings before a developer ever sees them. After the tool surfaces a potential vulnerability, a structured reasoning workflow hunts for logical gaps, unsupported assumptions, and conditions that would prevent the attack from succeeding. Only findings the engine fails to rule out reach a human reviewer — and when they do, VulnHunter delivers not just an alert but a full explanation of the exploit path and a proposed code fix ready for engineering review.

The tool currently runs on Anthropic's Claude Opus 4.8 model inside a Claude Code environment, though Capital One says the framework has the potential to work across other foundation models and coding harnesses.

Why Capital One is giving the tool away

Asked why Capital One decided to open-source a tool this consequential, Nims pointed to the communal nature of the problem.

"We felt an imperative to open-source VulnHunter because modern software supply chains are very connected, and the scale of the AI threat is larger than any single organization," Nims told VentureBeat. "Securing software and our digital environments is a shared foundation that benefits developers, enterprises, and the people who depend on the systems we all build. The defensive tools to address this reality need to be just as widely distributed, tested, and improved as the codebases they protect."

"Rather than wait," he added, "we decided that the right response was to build a product that is purpose-fit for today's complex security landscape, and put it into the hands of defenders everywhere."

Why Capital One believes open-sourcing VulnHunter strengthens everyone's defenses

The release nonetheless arrives against a backdrop the company knows well. On July 19, 2019, Capital One disclosed that an outside individual — later identified as a former Amazon Web Services employee named Paige Thompson — had gained unauthorized access to names, addresses, self-reported income, Social Security numbers, and linked bank account numbers belonging to credit card customers and applicants. The breach, which Capital One says occurred on March 22 and 23, 2019, was discovered only after an external security researcher flagged a configuration vulnerability through the company's Responsible Disclosure Program on July 17 of that year.

The damage was sweeping. Approximately 100 million people in the United States and 6 million in Canada were affected. Roughly 140,000 Social Security numbers, about 80,000 linked bank account numbers, and approximately 1 million Canadian Social Insurance Numbers were compromised. The FBI arrested Thompson, and the government stated it believed the data had been recovered with no evidence of fraud. But the reputational and regulatory toll was enormous.

In August 2020, the Office of the Comptroller of the Currency fined Capital One $80 million, finding that the bank had failed to adequately identify and manage risks as it migrated significant technology operations to the cloud. As Reuters reported at the time, the OCC's consent order cited insufficient network security controls, inadequate data loss prevention measures, and a board that failed to hold management accountable when internal auditing surfaced problems. The OCC also ordered Capital One to overhaul its operations and submit new cybersecurity plans for regulatory review.

CyberScoop at the time called the incident "a cautionary tale for companies rushing to embrace new tech." Capital One's own CEO, Richard D. Fairbank, acknowledged the gravity of the moment. "While I am grateful that the perpetrator has been caught, I am deeply sorry for what has happened," Fairbank said at the time. "I sincerely apologize for the understandable worry this incident must be causing those affected and I am committed to making it right."

Inside Capital One's decade-long open-source strategy — and what VulnHunter adds to it.

What followed was not a retreat from technology but a doubling down — with security explicitly at the center.

Capital One began releasing open-source projects in 2014 and declared itself an "open-source first" company in 2015 as part of a broader technology transformation that began over a decade ago. The company has continued to invest in software supply chain security, open-source governance, and AI-driven defense. In August 2022, Capital One joined the Open Source Security Foundation as a premier member, earning a seat on the organization's Governing Board. Chris Nims, then EVP of Cloud & Productivity Engineering, framed the move as a natural extension of the company's operating philosophy. "As a highly-regulated company, we are seasoned in managing compliance and governance and advocate for standardization, automation and collaboration," Nims said in the OpenSSF announcement.

Behind that public commitment lay a substantial operational apparatus. Capital One's Open Source Program Office, now in its third iteration, manages open-source usage, contributions, and community building across the enterprise. The company has released more than 40 open-source projects and has made thousands of contributions to external open-source projects it depends on, according to the company. Those efforts address not just code dependencies but the entire software development lifecycle — DevSecOps tools, infrastructure, and the collaborative environments, both internal and external, that shape how software gets built and shipped.

VulnHunter is the most consequential product of that multi-year effort — and the release reflects a calculation increasingly common among large enterprises: that in security, giving away the tool is cheaper than fighting alone. "At the end of the day our goal is to make this product as broadly accessible as possible," Nims said.

The company argues that modern software supply chains are so deeply interconnected that a single vulnerability in a widely used open-source component can cascade across thousands of enterprises simultaneously. Proprietary defenses, no matter how sophisticated, cannot address a problem that is fundamentally communal. By releasing VulnHunter under a permissive license, Capital One invites the global security research community to stress-test, extend, and improve the tool — effectively crowdsourcing its own defense infrastructure while strengthening the broader ecosystem.

Inside VulnHunter's three-stage AI engine for finding exploitable code

For engineering leaders evaluating VulnHunter, the technical architecture is where the tool's ambitions become concrete. The workflow unfolds in three distinct stages.

In the first stage — attacker-first forward analysis — VulnHunter begins at the points where an external adversary would interact with a system: API endpoints, network message handlers, file upload interfaces. From each entry point, the tool reasons forward through application logic, tracing data flows, transformations, and internal security checkpoints to determine whether an attacker can actually reach a dangerous code path. This approach mirrors how a skilled penetration tester would probe a system, but automates the process at a scale no human team could match.

The second stage is where VulnHunter departs most sharply from conventional scanners. After identifying a potential vulnerability, the falsification engine runs a structured reasoning workflow designed to disprove its own conclusion. It searches for assumptions that do not hold, logical gaps in the exploit path, and environmental conditions that would prevent an attack from succeeding. Findings that fail this internal challenge are discarded before any developer sees them. Capital One's explicit goal is to shift the developer's burden away from triaging false alarms — a perennial pain point that erodes trust in security tooling and slows development velocity.

In the third stage, vulnerabilities that survive the falsification engine trigger an evidence-backed remediation workflow. VulnHunter gathers supporting evidence across the codebase, maps the complete surviving exploit path, explains the defect and the specific capabilities an attacker would gain, and generates targeted code changes for engineering review. The output is not a generic advisory but a concrete, context-aware patch proposal.

Capital One says it validated VulnHunter internally before release, running it across thousands of repositories spanning tens of business areas. The company reports that the tool identified and remediated vulnerabilities with speed and efficiency that far exceeded what its teams previously achieved through manual triage.

Why AI-powered attacks are forcing banks to rethink traditional cyber defenses

VulnHunter arrives at a moment when the cybersecurity landscape is shifting beneath the feet of every enterprise. Capital One's announcement frames the urgency in stark terms: advanced AI models have "dramatically lowered the barrier for bad actors to discover and exploit vulnerabilities in software," and the window before sophisticated AI attack capabilities become affordable and accessible to virtually every adversary is shrinking rapidly.

"Safeguarding information is essential to our mission and our role as a financial institution," Nims told VentureBeat. "We have invested heavily in cybersecurity and will continue to do so to stay ahead of today's evolving threat landscape."

The company's own AI security researchers have been tracking these trends closely. At NeurIPS 2024 in Vancouver, Capital One's team presented research and curated a list of nearly 100 papers spanning LLM safety, adversarial resilience, jailbreak attacks, and synthetic data generation. The papers they highlighted — including work on multi-agent defense frameworks, automated red-teaming, and guardrail classifiers — paint a picture of an arms race in which offensive and defensive AI capabilities are co-evolving at breakneck speed.

Several of those research themes map directly onto VulnHunter's architecture. The falsification engine echoes the adversarial defense strategies explored in papers like "BackdoorAlign," which demonstrated that embedding a structured safety mechanism into a small number of training examples could recover a model's safety alignment without degrading performance. The attacker-first forward analysis reflects the philosophy of "WildTeaming," a framework that collects and analyzes real-world jailbreak attempts to build more resilient models. And VulnHunter's emphasis on minimizing false positives parallels the goals of "GuardFormer," a guardrail classifier that outperformed GPT-4 on safety benchmarks while running 14 times faster.

The thread connecting all of this work is a conviction that traditional, reactive security — monitoring networks, patching known vulnerabilities, responding to incidents after they occur — is no longer sufficient when adversaries can use AI to discover and exploit zero-day vulnerabilities at machine speed. The only durable defense, Capital One argues, is to find and fix the vulnerabilities in your own code before attackers find them first.

What Capital One's cloud security journey reveals about the entire banking industry

Capital One's cloud journey also illuminates a broader reckoning across financial services. When Capital One moved aggressively to Amazon Web Services in the mid-2010s, it was a rarity among major banks. Most financial institutions simply did not trust third parties to store their most sensitive data. Capital One's CIO at the time, Rob Alexander, publicly championed the cloud as more secure than the bank's own data centers — a claim that the 2019 breach complicated considerably.

The CyberScoop report from that period captured the tension within the industry. W. Patrick Opet, managing director of cybersecurity at JP Morgan Chase, described a cultural shift in banking from prioritizing traders to prioritizing developers: "Now, it's 'Focus on the developer, turn everything into code, and automate everything.'" Mark Nicholson, Deloitte's cyber leader for the financial industry, noted that the pressure to move quickly was exposing "weaknesses in the development methodology." And the breach itself was a reminder that even as Chase spent $600 million annually on cybersecurity, relatively simple vulnerabilities — like the Apache Struts bug that enabled the Equifax breach — could undercut massive investments in data protection.

Seven years later, the industry has largely followed Capital One into the cloud, and the security challenges have only intensified. The question is no longer whether to use cloud infrastructure but how to secure the software that runs on it. VulnHunter represents part of Capital One's answer to that question: rather than relying solely on network-level controls and perimeter defenses, push security directly into the code itself, at the moment it is written. The open-source release also carries implicit competitive pressure. If VulnHunter gains traction among developers and security teams, it could set a new baseline for what enterprise security tooling is expected to do — and force rival banks, fintechs, and cloud providers to match or exceed its capabilities.

Whether VulnHunter lives up to that ambition will depend on adoption, community engagement, and the tool's real-world performance against the increasingly sophisticated AI-powered attacks it was designed to counter. But the release itself tells a story that extends well beyond any single tool or any single company. In 2019, a misconfigured web application firewall — exploited alongside an overly permissive cloud access role — exposed the records of roughly 100 million Americans, drew an $80 million regulatory fine, and became one of the most widely cited cloud security incidents in the industry.

In 2026, the same institution is open-sourcing an AI-driven defense built for a new generation of threats — and betting that the best way to protect its own code is to help the entire industry protect theirs.

Intuit was an early pioneer in the usage of agentic AI, but its path to success has hardly been a straight line.

At VB Transform 2026, Intuit VP of AI Nhung Ho described how the company rebuilt its agent architecture twice in the span of about four months, first moving from a fleet of specialist agents to a central orchestration layer, then abandoning that layer for a skills and tools based system once the orchestrator itself started failing under its own complexity. The full second rebuild took 60 days, with a first working version in under 20.

The failure mode that forced the second rewrite was specific. Agents in the orchestrated system passed results to each other in natural language, and each handoff lost context the next agent needed to act correctly. 

"If you have 10 agents and they all are passing to each other, every time that pass happens, error compounds," Ho said.

Why the orchestration layer broke down

Ho said the original push toward specialist agents came from a straightforward customer complaint. A fleet of capable agents is still something a customer has to manage, deciding which agent to use for which task. Intuit's answer was a system that could take a task and route it internally, without asking the customer to pick an agent themselves.

That orchestration layer held up for about three months, which Ho described only half joking as roughly a year in the compressed timeline of agent development in 2026.

It broke for a structural reason rather than a capacity one. Passing outcomes between agents in natural language meant each downstream agent had to infer how the upstream agent reached its conclusion, and that inference degraded with each additional hop. A ten agent chain did not fail occasionally, it compounded errors by design.

That diagnosis is what sent Intuit back to a skills and tools architecture.

The 60-day rebuild, and what it took to get engineering buy-in

Rebuilding a production agent system in 60 days required more than an architectural decision. Ho said the harder problem was internal, convincing both leadership and the engineers who had built the original agents that scrapping recent work was the right call.

The pitch to leadership relied on evidence rather than argument. Ho's team built a demo of the new architecture using real customer queries pulled from production, then showed it performing better than the existing system on the same tasks. 

"The best proof, at least my belief, is what are customers trying to do? And whatever system you build needs to address those problems," Ho said.

Winning over engineering required a different case. Hundreds of engineers outside Ho's core team had built the specialist agents being retired, and the ask was to take their agents apart into individual skills and tools instead. 

Ho said the motivating argument was scale. A standalone agent solved one narrow problem, while a shared skill or tool built into the new architecture could serve every customer who touched that part of the product. That shift also changed what partner teams were responsible for day to day, moving their focus from building agents to running evals, since evals became the only way to measure whether the new architecture was actually working.

Bringing a human into the loop, and feedback at a different scale

The clearest customer facing result of the rebuild is a feature that lets a live agent conversation pull in a human — though it's currently in early testing, live to about 1% of Intuit's customer base. "We're going to be scaling it up in the next few weeks," she said.

Ho said a customer can bring in an Intuit product support person mid conversation, or their own accountant, or one of Intuit's own bookkeepers, and that person joins with the full context of what the agent has already done.

Ho drew a direct contrast with how most AI chat products handle the same situation. A general purpose assistant answering a tax question typically ends with a disclaimer to consult a professional. Intuit's system is built to connect the customer to that professional directly, inside the same conversation.

That human handoff sits alongside a permissions model built for financial data specifically. Every action an agent takes on a customer's financial data requires explicit permission first, though Ho said that requirement can ease over time as customers build trust in the system. Intuit keeps an audit log of everything an agent does that can be reversed if needed.

Feedback in the agentic AI era

The rebuild also changed how Intuit gathers and uses feedback, a shift Ho said is qualitatively different from what came before. 

"Feedback in the past used to be very, very sparse, and it was also very bimodal," Ho said. "Either they loved it or they hated it, and usually it tends towards the negative."

In a chat based system, every conversation functions as feedback, which Ho said moved the company from roughly 0.3% of customers ever giving explicit feedback to something close to 100%.

Ho said she has returned to writing code herself specifically to build models that analyze that feedback volume systematically, looking for where the system is falling short at a scale no manual review process could keep up with.

That volume comes with a tone most product teams aren't used to hearing directly. Customers tell the agent exactly where it failed, in plain terms.

"They straight up tell you, 'You suck. I hate this. This is not right,'" Ho said. "But they're also willing to give the systems grace and correct it as well, and so the onus is on all of us to harvest this new piece of feedback and type of feedback, and actually improve the system."

Legacy infrastructure, not the models themselves, is what's actually slowing AI agents down. That was the shared conclusion of three infrastructure leaders — from LinkedIn, Walmart, and Zendesk — at VB Transform 2026.

The panel brought together Animesh Singh, senior director of AI platform and infrastructure at LinkedIn, Desiree Gosby, SVP of corporate technology services and technology strategy at Walmart, and Sami Ghoche, VP of applied AI at Zendesk, each describing what actually broke when they moved agents from pilot to production. Each arrived at the same conclusion from a different starting point: None of the bottlenecks they hit were model problems.

What tied their answers together was a shared premise: most enterprise infrastructure was built for how humans work, not for how agents work. The gap between those two speeds is where the real engineering happened.

Gosby put it plainly when asked what she'd learned scaling agents inside Walmart's own workforce. The goal, she said, is to make sure "engineering doesn't once again become the bottleneck for what it is we're trying to do."

Where the bottleneck actually was

Each company hit a different version of the same wall: infrastructure designed for how people work doesn't hold up once agents are doing the work instead.

At LinkedIn, the first bottleneck wasn't a model, it was Kubernetes, which assumes containers spin up on demand, a process that takes seconds. Singh said that's too slow for agents. The fix was moving from on-demand provisioning to pre-provisioned pools of containers that swap agentic workloads in and out in real time.

A second, harder problem surfaced once LinkedIn let agents control their own orchestration. A five-point evaluation system looked clean, but hallucination kept showing up anyway. Singh said the issue was structural, an LLM evaluating another LLM's output shares the same failure mode as the thing it's evaluating. 

"We built our own harness, our own control flow, and pushed the LLMs to the leaf instead of them orchestrating the loop," Singh said. Roughly 80% of the workflow is now scripted, deterministic code, with LLMs used only where reasoning is required, and each step's evidence is committed to disk before the system moves on.

Walmart's bottleneck came from success. An agent harness put directly into employees' hands went viral internally, and what Gosby called "citizen developers" began building their own agents to solve problems that once required a formal engineering roadmap. The upside was real innovation. The downside was duplication, dozens of overlapping agents with no coordination. The fix wasn't reining in the harness, it was building governance to spot duplication, promote the best version of an agent, and get it into production without engineering becoming a chokepoint.

Zendesk hit its bottleneck from the data side. Ghoche, who joined through Zendesk's acquisition of Forethought, which closed in March 2026, described sitting on what he called a public figure of 20 billion customer conversations in Zendesk's repository. The instinct is to hand that history to a large language model with a big context window and let it generate the agents a business needs. Ghoche said that doesn't work. "You can't really do that, so instead you have to really invest in the underlying data pipelines and all the data infrastructure that comes with that," he said.

The role of open source

On open source, all three leaders landed on a similar instinct: own what you can, and lean on frontier labs only where they still have a clear edge.

Ghoche said his own view is that most enterprises would prefer to own their models and infrastructure wherever that's possible, and that reasoning is what drives Zendesk's own approach. The exception is frontier reasoning work, where the labs still lead, though he said that slice of use cases is shrinking relative to everything else enterprises now do with AI.

LinkedIn's answer was to build two subsystems specifically for independence. The first is what the company calls an AI gateway, a single interface that every outbound call to a model runs through regardless of provider. The second component is a memory subsystem built to hold context independent of any model provider.

"Every single outbound call going to an LLM, whether it's on a public cloud or on-prem in our own data centers, follows the same semantics, the same API calls. We can quickly switch between different providers," Singh said. 

Walmart built its own internal gateway to stay vendor agnostic across three workload types: fully deterministic workflows, planner-and-reasoner workflows for open-ended tasks, and a hybrid of the two. Compliance-heavy work stays deterministic by design; governance, security and evaluation run through the gateway regardless of which model is on the other end. Gosby said the choice between a frontier model and an open-weight model comes down to whichever is most effective for the specific workload, not a fixed policy.

Advice for the modernization journey

Three pieces of advice came up directly, each tied to the wall a leader had already hit.

Invest in evals before anything else. Ghoche called it the thing common to every use case, internal or customer facing. 

"The thing that's common to all of these is evals. It'll force you to break the problem down, and once you have a robust set of evals, you can move a lot faster," he said, 

Own your agent harness from day one. Gosby's advice was to put the AI harness directly in employees' hands early, paired with the infrastructure to monitor what it produces. 

"It will unlock a huge amount of innovation," she said.

Build for model and context independence. Ensuring flexibility is critical for success.

"Build for independence, whether it's a frontier model of today versus an open source model of tomorrow," Singh said. "Keep that context within your enterprise so that you can reuse it when you ship the model or the harness tomorrow," Singh said.

OpenClaw has become one of the most widely adopted agentic frameworks, but it has yet to prove itself at enterprise scale. Agents need real credentials — API keys, OAuth tokens, service accounts — to work effectively, and Brex found that traditional guardrails couldn't contain what those agents were doing with them.

Brex set out to overcome these limitations by building an internal platform it calls CrabTrap. The open-source HTTP/HTTPS proxy intercepts all network traffic, examines policy rules, and uses a LLM-as-a-judge to decide whether agent requests should be approved or denied. 

“What we noticed was that the network layer was an untapped enforcement point,” Brex co-founder and CEO Pedro Franceschi told VentureBeat. “Every request an agent makes is an opportunity to intercept, reason about, and make a policy decision.”

The takeaway Franceschi wants IT leaders to draw: agent governance should shift from SDK-level permissions and model guardrails toward a centralized network control plane that enforces and learns from real in-the-wild agent behavior.

How Brex targeted the transport layer

The “obvious fix” (at least initially) to the agent security gap was guardrails, and much of the early work has centered on scoped tools, per-action permissions, and human-in-the-loop approvals. But as agents evolve, each new capability means there’s another API to tune or surface to audit, Franceschi noted. 

“Any agentic system with multiple tools and access to the open internet creates an immediate tension for builders: The more capable you make an agent, the more dangerous it becomes, and the safer you make it, the less useful it is,” he said. 

Existing solutions to this tradeoff were “weak”: Fine-grained API tokens help at the margins but can still be misused and constrain functionality. Semantic guardrails (such as context, skills, or prompt steering) are easily bypassed by prompt injection, especially for agents connected to the internet.

Agents can be “defanged” when given read-only access or limited toolsets, but then they can't do meaningful work, Franceschi said. On the other hand, granting broad write access and a large tool surface can result in hallucinations and real production consequences.

Model context protocol (MCP) gateways enforce policy at the protocol layer — but only for traffic using MCP. Meanwhile, guardrails from LLM providers are tied to a single model and can be “opaque” to customize with enterprise-specific policies. And powerful tools like Nvidia OpenShell offer more of a “per-sandbox egress control.”

“When we started, we hadn’t found a solution to deploying harnesses like OpenClaw safely,” Franceschi said. “Instead of waiting for the industry to catch up, we decided to own the problem and invent the necessary tools.”

Notably, they needed a platform that sat between every agent and every network request, and could make “nuanced decisions about what to allow,” he said. 

This made the transport layer a core architectural component and natural starting point, he said. 

By operating at this layer, CrabTrap is framework-agnostic, language-agnostic, and API-agnostic. It doesn't require SDK wrappers or per-tool integration. Users set HTTP_PROXY and HTTPS_PROXY in the agent's environment, and every outbound request routes through the proxy before it reaches a destination.

However, Franceschi emphasized, Brex didn't start at the transport layer because it thought it was the only answer; rather, they believe in “security by layers.”

“The transport layer was simply an underinvested one, and we saw an opportunity to add meaningful enforcement there alongside everything else,” he said. 

The LLM-as-a-judge training loop

CrabTrap combines deterministic static rules with an LLM-as-a-judge for requests that fall outside known patterns, Franceschi explained. The judge only “fires on the long tail of unfamiliar endpoints or unusual request shapes,” which for a mature agent is typically fewer than 3% of requests.

The more pressing problem was how to know that a policy is the right one? With static rules, it's “relatively straightforward” to reason about accuracy. But with an LLM judge, the system is nondeterministic, and users need confidence that the policy approves the right requests and blocks the rest.

“Our key insight was to bootstrap policy from observed behavior rather than write it from scratch,” Franceschi said. Beginning with real behavior and editing down based on real-world learnings turned out to be “dramatically more effective than starting from a blank page.”

Brex’s team built a policy builder (itself an agentic loop) that runs underlying agents in shadow mode, analyzes historic network traffic, samples representative calls, and drafts a natural-language policy that matches what the agent actually does. 

From there, they built an eval system that tests policy changes before they go live. CrabTrap compares historical audit entries against a draft policy and reports the exact changes to be made. Users can slice results by method, URL, original decision, and agreement status. 

All of this runs with concurrent judge calls, so replaying thousands of requests “takes minutes, not hours,” Franceschi said. Brex also developed a live feedback loop: Full audit trails are stored in PostgreSQL and queryable through the admin API and dashboard. In cases where a resource is continuously denied, the system can notify a human or an agent to propose a policy update for review. 

“That closes the loop between observed denials and policy refinement,” Franceschi said. 

Core challenges and roadblocks 

Of course, the build wasn’t without its challenges. A big one was latency: “Putting an LLM between an agent and every outbound API request sounds like it would grind things to a halt,” he said. 

However, it didn’t turn out to be as big a problem as expected. This was for two reasons: The LLM judge only activates on a small fraction of requests (the aforementioned 3%). Agents quickly settle into predictable traffic patterns; once observed, high-volume patterns become static rules. Second, by using small, fast models like Claude Haiku meant that, even when the judge did fire, added latency was “negligible.” This can be further reduced with local models and prompt caching, Franceschi said.

The harder and less obvious challenge was prompt injection, he said. The judge receives the full HTTP request and all content is user-controlled, so potentially, a crafted URL, header, or request body could manipulate the judge's decision. 

Brex addressed this by structuring the request as a JSON object before sending it to the model, so all user-controlled content is “escaped rather than interpolated as raw text,” Franceschi said. 

Results, and where CrabTrap might evolve

Brex tracks a few factors to measure CrabTrap’s internal impact: Engagement with agents, network traffic patterns, and net promoter scores (NPS). The most meaningful result of CrabTrap has been “organizational confidence,” Franceschi said. 

Previously, the team had “real hesitation” when it came to deploying autonomous agents broadly across business operations, because the existing guardrail options didn't provide enough assurance. 

“CrabTrap changed that calculus,” Franceschi said. They now have an enforcement layer they trust, increasing confidence around expanding agent deployment into more parts of the business and delegating more agent configuration and management to users. 

Franceschi described the policies derived from traffic as “surprisingly strong.” The team expected the policy builder to produce a “rough starting point” requiring heavy manual editing. In practice, though, pointing the platform at a few days of real traffic produced policies that matched human judgment on the “vast majority of held-out requests.”

Additionally, CrabTrap revealed how much noise agents generate. “The audit trail made this visible for the first time,” Franceschi said. They used denial logs and traffic analysis not only to tune policies, but to tighten agents themselves, remove tools, and cut out entire categories of requests that were wasting both time and tokens.

“The proxy became a discovery tool, not just an enforcement one,” he said. 

Areas for growth (and input from the open-source community)

Brex anticipates CrabTrap to continue to evolve, particularly as they have released it as open-source. “We hope the community helps shape it,” Franceschi said. 

Areas of improvement include deeper authentication functionality such as single-sign on (SSO), fine-grained role-based access control (RBAC); escalation workflows that allow agents to request additional permissions; and policy recommendations based on denial patterns.

Programmatic configuration, or developing API endpoints for “creating, forking, and applying” policies to agents, could allow the whole policy lifecycle to be automated rather than managed manually, Franceschi said. 

As for escalation, if an agent is continuously denied a given resource or endpoint, it should be able to route requests to humans or other AI agents for review and back that up with a rationale for why it needs access. 

“That turns CrabTrap from a hard enforcement boundary into something more like a managed permission system,” Franceschi said. 

Additionally, the policy was built to bootstrap from network traffic, but there is opportunity to incorporate additional signals around agent traces and resource-calling, as well as broader context on what agents are ultimately trying to accomplish. This can help produce more accurate and nuanced policies. 

Finally, there's an “open philosophical question” about the right posture for CrabTrap: Should it be a fully transparent layer that the agent itself is unaware of, or should it operate more like a “well-intentioned manager”? (that is, the agent knows about the layer and can interact with it).

The open-source community can help shape these developments, and CrabTrap will only get better with more users, Franceschi said. Brex’s agents speak to a specific set of APIs; teams using CrabTrap with different agents, services, and policy requirements will surface “edge cases and patterns we can't hit alone.”

“We have ambitious plans for where it could go, and we’d rather build in the open,” Franceschi said. 

What other builders can learn from CrabTrap

The response has been stronger than expected. CrabTrap has more than 700 stars on GitHub. Franceschi said Brex has also heard from OpenAI, Y Combinator CEO Garry Tan, and programmer Pete Steinberger, all expressing interest in deploying similar internal infrastructure.

The broader lesson: “Don't let infrastructure gaps become excuses to wait," Franceschi advised. There are “real blockers” for every enterprise looking to seriously deploy AI agents, including security concerns, lack of tooling, or unclear guardrails. 

“It's tempting to sit on your hands until the industry catches up,” he said. “The lesson from CrabTrap is that you can own those problems directly.”

Moonshot AI, the Beijing-based artificial intelligence startup backed by Alibaba, on Thursday released Kimi K3 — a 2.8-trillion-parameter model that the company says is now the largest open-source AI model in the world, and one that benchmarks show performs neck-and-neck with the most powerful proprietary systems from Anthropic and OpenAI.

The release, timed to land just ahead of the 2026 World Artificial Intelligence Conference in Shanghai, is a dramatic escalation in the global AI arms race and a watershed moment for the open-source AI movement. It also marks a remarkable comeback for a company whose market position had eroded significantly over the past 18 months following DeepSeek's meteoric rise.

Full model weights are scheduled to be released on July 27, according to details shared by researchers who reviewed the company's technical documentation. If you want to take Kimi K3 for a spin right now, you can — just head to kimi.com, sign up with a Google account or phone number (no credit card required), and start chatting with what may be the most powerful open-source model ever built.

Inside the architecture that powers the world's largest open-source AI model

Kimi K3 is a frontier-class large language model with 2.8 trillion total parameters — roughly 75 percent larger than DeepSeek's V4 Pro, which the company's own timeline chart shows at approximately 1.6 trillion parameters. The model features a 1-million-token context window, native visual understanding capabilities, and an always-on reasoning mode that the company calls "thinking mode."

The model is built on two key architectural innovations developed internally at Moonshot AI: Kimi Delta Attention, a hybrid linear attention mechanism, and Attention Residuals, which the company describes as a drop-in replacement for residual connections that delivers consistent scaling gains. Both techniques were previously published as open research by the Moonshot team on GitHub.

On the API side, Kimi K3 is compatible with the OpenAI SDK, lowering the integration barrier for developers already building on OpenAI or Anthropic toolchains. The model is priced at $3 per million input tokens and $15 per million output tokens, with cached input tokens dropping to just $0.30 per million — pricing that positions it roughly in line with mid-tier offerings from Western labs, but at a performance level the company claims approaches the top of the market. A promotional top-up rebate running through August 12 offers up to 30 percent back in vouchers for API credits of $1,000 or more.

As Xinhua reported, a Moonshot AI executive explained the significance of the parameter count in simple terms: parameters are like neural connections in the human brain, and nearly 3 trillion of them means the model can "store more knowledge and patterns in its brain, understand more, think deeper, and answer more accurately."

Benchmark results show Kimi K3 trading blows with Claude and GPT at the top of the leaderboard

The benchmark results, drawn from public leaderboard data and a private evaluation by analytics firm Artificial Analysis, tell a striking story.

On GDPval-AA v2, a benchmark measuring real-world tasks across 44 occupations and 9 major industries, Kimi K3 scored 1,687 — placing it third overall, behind only Claude Fable 5 Max (1,815) and GPT-5.6 Sol Max (1,747.8), and ahead of Claude Opus 4.8 (1,600).

On AA-Briefcase, a private agentic benchmark from Artificial Analysis designed to test long-horizon knowledge work, K3 climbed to second place with a score of 1,527 — beating GPT-5.6 Sol Max (1,495) and trailing only Fable 5 Max (1,587).

Perhaps most impressively, K3 achieved a state-of-the-art score of 91.2 out of 100 on BrowseComp, a benchmark for long-horizon, high-difficulty information seeking.

The company says it accomplished this in a single-agent setup using its 1-million-token context window, without any context compression or additional context management techniques — a feat that suggests raw context length, when paired with strong retrieval capabilities, may be more powerful than elaborate multi-agent workarounds.

As one widely followed AI commentator put it on social media: "Open source is no longer lagging six months behind Western closed-source models. Read that again, and think about what it all means."

That observation captures the significance of the moment. For much of the past three years, open-source models have typically trailed their proprietary counterparts by a meaningful margin. Kimi K3 appears to have closed that gap almost entirely.

How a 48-hour autonomous chip design demo reveals Moonshot's real ambitions

Beyond raw benchmarks, Moonshot AI showcased a proof-of-concept that may be even more revealing of K3's capabilities and the company's strategic direction.

In a demonstration documented in the company's technical materials, Kimi K3 was tasked with designing a physical chip to run a nano-scale version of itself. Over 48 hours of continuous autonomous agent operation, K3 independently completed the chip's full construction pipeline — from architectural design through optimization and verification — using open-source electronic design automation tools. The result was a tiny but functional chip design, just 4 square millimeters, that achieved timing convergence at 100 MHz and could decode more than 8,700 tokens per second in simulation.

This is not a production chip. It is a demonstration of what Moonshot AI clearly views as the next competitive frontier: long-range autonomous agent capabilities. The ability to sustain coherent, multi-step technical work over a 48-hour window — reading documentation, making design decisions, running verification loops, and iterating on failures — represents a qualitative leap beyond the kind of single-turn question-answering that defined the first generation of large language models.

The company also highlighted a case in computational astrophysics, where K3 reportedly reproduced the universal I-Love-Q relation — a complex calculation that typically takes a senior researcher one to two weeks — in approximately two hours, reading and cross-validating more than 20 papers and implementing a complete numerical pipeline along the way.

Moonshot AI's fall and rise tells the story of China's brutal AI market

To understand why Kimi K3 matters, you need to understand where Moonshot AI was 18 months ago — and how far it fell.

Founded in 2023 by Yang Zhilin, a Tsinghua University graduate who previously conducted research at Google and Meta, Moonshot AI quickly became one of China's most prominent AI startups. The company gained early traction in 2024 when users flocked to its Kimi platform for its long-text analysis capabilities and AI search functions. By early 2026, it had raised roughly $1.5 billion across multiple rounds, with its valuation climbing from $2.5 billion to $4.3 billion and the company reportedly seeking a new round at $5 billion.

Then DeepSeek happened. The release of DeepSeek's low-cost R1 model in January 2025 disrupted the entire Chinese AI landscape, and Moonshot AI was among the hardest hit. Kimi, which had ranked third in monthly active users in China, slid to seventh. The company's strategic pivot to open-source models — beginning with Kimi K2 in July 2025 and accelerating with K2.5 in January 2026 — was in large part an effort to reclaim relevance.

Kimi K3 is the culmination of that effort — and the sheer scale of the model suggests that Moonshot AI has been planning this move for some time. Training a 2.8-trillion-parameter model requires enormous computational resources and months of preparation, which means the architectural and infrastructure decisions behind K3 were likely locked in well before the model reached the public.

Why open-sourcing the world's biggest model is a geopolitical chess move

The decision to release K3's full weights on July 27 is strategically significant and worth parsing carefully.

The company's own timeline chart of open-source frontier model scale positions K3 as a dramatic outlier, towering above competitors like DeepSeek (1.6T), Xiaomi (1.02T), and Alibaba (397B). By releasing the world's largest open-source model, Moonshot AI is making a bid to become the center of gravity for the global open-source AI developer community.

This follows a broader trend among Chinese AI companies. As Reuters noted, open-sourcing allows companies to "showcase their technological capabilities and expand developer communities as well as their global influence, a strategy likely to help China counter U.S. efforts to limit Beijing's tech progress." DeepSeek, Alibaba, Tencent, and Baidu have all released open-source models. But none have released anything at this parameter count.

For enterprise technology leaders, the implications are concrete. A 2.8-trillion-parameter open-source model that performs at near-frontier levels creates new options for companies that want to fine-tune, self-host, or build proprietary systems on top of a capable base model — without being locked into API contracts with OpenAI or Anthropic. The trade-off, of course, is that running a model of this size requires substantial GPU infrastructure. Inference at 2.8 trillion parameters is not something that runs on a single server rack.

That said, Moonshot AI has signaled awareness of this challenge. Its Mooncake project, which won the Best Paper award at FAST 2025, pioneered KV-cache-centric disaggregated serving for large language models — an architecture designed specifically to make inference at extreme scale more practical and cost-efficient.

Kimi Code and a three-tier model lineup form the foundation of Moonshot's enterprise play

Alongside K3, Moonshot AI continues to invest heavily in its coding agent ecosystem. Kimi Code, the company's open-source coding tool that competes with Anthropic's Claude Code and Google's Gemini CLI, received two major updates on the same day as K3's launch — versions 0.25.0 and 0.26.0 — adding features like expanded subagent tooling, background task management, and security fixes.

The Kimi Code CLI has accumulated over 3,100 stars on GitHub and features integration with VSCode, Cursor, and Zed. The latest release expanded the "coder subagent" tool set to include background tasks, todo lists, plan mode, skill invocation, and nested agents — effectively turning the coding agent into a multi-layered autonomous system capable of managing complex software engineering projects with minimal human intervention.

This is not incidental. Coding tools have become a critical revenue driver for AI labs. As Anthropic disclosed in January, Claude Code reached $1 billion in annualized recurring revenue. By building Kimi Code as an open-source alternative that defaults to Kimi's own models — but supports other providers — Moonshot AI is positioning itself to capture developer workflows and, eventually, enterprise contracts.

The company's model lineup now includes three tiers: K3 as the flagship ($3/$15 per million tokens for input/output), K2.7 Code as a specialized coding model ($0.95/$4), and K2.6 as a general-purpose option ($0.95/$4). All three support context windows of 256,000 tokens or above, with K3 offering the full 1-million-token window. Context caching is automatic — no cache ID, TTL, or extra parameter is required — a small but meaningful developer-experience advantage over competitors that require explicit cache management.

What Kimi K3 means for the future of enterprise AI and the global model landscape

Kimi K3's release forces a recalibration of several assumptions that have guided enterprise AI strategy.

The performance gap between open-source and proprietary models has functionally closed at the frontier. If K3's benchmark numbers hold up under independent evaluation — and particularly once the open weights are available for community testing on July 27 — it will be difficult for closed-source providers to justify premium pricing purely on the basis of capability.

The locus of AI innovation, meanwhile, continues to shift. China's AI ecosystem, which many Western observers questioned after early struggles with chip export restrictions, has now produced a model that competes with the best systems from companies with direct access to Nvidia's most advanced hardware. The architectural innovations behind K3 — particularly the hybrid linear attention mechanism — suggest that algorithmic efficiency may matter as much as raw compute.

And the agentic capabilities demonstrated by K3 — chip design, multi-week research compression, long-horizon information seeking — point toward a future where AI models are not just answering questions but autonomously executing complex, multi-day projects. For enterprises evaluating AI investments, this shifts the value proposition from "productivity copilot" to "autonomous technical workforce."

Xinhua, China's state news agency, framed the release as a national milestone, reporting that K3 "marks a new step forward in the development of China's artificial intelligence models." Liu Tieyan, dean of the Zhongguancun Academy in Beijing, was quoted as saying that a wave of Chinese open-source models has moved from isolated breakthroughs to collective advancement, providing "new solutions and new paths" for global AI development.

Just two years ago, Moonshot AI was a scrappy startup named for the audacious problems it hoped to solve. Eighteen months ago, it was a cautionary tale about how quickly a market darling can lose its footing. Today, it is the maker of the world's largest open-source AI model — one that can, given 48 hours and an internet connection, design a chip to run itself. The frontier, it turns out, is not a place. It is a race. And the field just got a lot more crowded.

Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whether their defenses are keeping pace with AI-enabled attackers. The result is an agent security gap — autonomous agents proliferating faster than the identity, isolation, and enforcement controls needed to hold them.

This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers.

The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius.

What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%).

At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators.

Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents.

Finding 1: The incidents are already here

More than half have had an agent security incident or near-miss

We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had.

This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss.

Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius.

Finding 2: The identity gap

Only a third give every agent its own scoped identity

We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception.

Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.)

The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security.

Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance.

Finding 3: Observe and enforce, but rarely isolate

Only three in 10 sandbox their highest-risk agents

We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common.

Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates.

Finding 4: Security runs on borrowed, provider-native controls

Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register

We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors.

Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale.

The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here.

(A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.)

Finding 5: And enterprises are comfortable with it

Satisfaction is high, even as incidents mount and identity lags

We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above.

Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies.

Finding 6: Budgets haven’t caught up

Most spend under a tenth of the security budget on agents

We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest.

Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not.

Finding 7: The arms race is even, at best

Only a third think their AI defenses are ahead of AI-enabled attackers

We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled.

Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be.

Finding 8: A security reshuffle is coming

Nearly six in 10 plan to adopt or switch tooling within a year

We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat.

The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle.

Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism.

The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint. 

What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking.

The bottom line: A security gap that autonomy will test first

Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents.

The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences.

Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap — heavy, fast-moving investment running ahead of the visibility needed to control it.

This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all.

The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own.

Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter — unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions — the shift from GPU compute to memory bandwidth as inference scales — is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it.

This report is one of five in VentureBeat Research's Q2 study of the agentic stack. Cost is the control with the least instrumentation: More than eight in ten GPU operators report utilization at half capacity or less, and a minority rigorously track compute cost and return. The executive summary, "VentureBeat Research: Where enterprise AI agent governance hasn't caught up," places that finding in the full pattern. 

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By organization size the sample concentrates in the mid-market: 101–250 employees (36%) and 251–1,000 (27%) lead, with 1,001–5,000 (22%), 5,001–10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%).

At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators.

Finding 1: Ambition outpaces production

Only one in five run AI in production at scale

We asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale.

The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint — and whose costs — are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works.

Finding 2: Enterprises run on hyperscalers and model APIs

The specialized GPU clouds barely register — today

We asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents.

The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from — which makes the evaluation intentions in Finding 3 all the more striking.

(A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses — an average of 2.1 selections each — so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.)

Finding 3: The next dollar goes to infrastructure they don’t yet run

AI-specialized clouds top the evaluations list

We asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today.

Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud.

This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal — CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use.

Finding 4: A switching wave is building

Six in 10 plan to change providers within a year — many within a quarter

We asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still.

For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months — and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents — Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) — which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share.

(Method note: Respondents who selected both "no plans to change" and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.)

Finding 5: Nobody buys on token price

Integration and total cost of ownership decide — not sticker price

We asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last.

Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric — cost per million tokens — is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 — enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step.

Finding 6: Expensive GPUs, idle most of the time

83% report GPU utilization of 50% or less

We asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency.

Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50%

The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large — and largely unmeasured.

Finding 7: Spending fast, measuring slowly

Fewer than half rigorously track what their compute costs

We asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending.

Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion — enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly — the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly.

Finding 8: The next bottleneck few are watching

As inference shifts from compute to memory, the field scatters

Finally, we asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority.

The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market — and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one.

The bottom line: A compute gap that faster spending will widen, not close

Organizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack — toward specialized clouds and alternative accelerators almost none of them run today — and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly.

The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money — the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters — but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or buy the next layer of infrastructure as blind to its economics as the last.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement — the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries.

Presented by Ping Identity

Enterprises need to treat zero trust security architecture as an immediate requirement for AI agents rather than a long-term goal, says Andre Durand, CEO and founder of Ping Identity. Zero trust, the security model built on the assumption that no user, device, or system should be automatically trusted, requires continuous verification before every action rather than a single check at login. Agentic AI has profoundly compressed the risk timeline enterprises must manage, demanding that permission decisions be evaluated in real time.

type: embedded-entry-inline id: 1Ieiy1KhHNWZE5KVqNdA1G

That compression shows up in how permissions accumulate. Every time an employee approves an AI agent's request for access to a company drive, a database, or a code repository, the enterprise hands over a sliver of control that looks routine in isolation. Across thousands of agents making thousands of requests, those approvals accumulate into an exposure that most existing security architectures were never built to measure.

"The rise in desire to use agents right now, and the speed of agentic, is highlighting the need to move faster on the principles of zero trust," Durand says. "Agents just move faster, full stop. A human compromise might be measured in minutes or hours, sometimes days. At agentic speed, a thousand actions could happen in five minutes."

Why zero trust is now urgent for agentic AI

That difference in velocity changes how enterprises need to think about permissions. Two variables matter: the surface area of access an agent is granted and the duration that access remains valid. Traditional identity and access management tends to grant broad permissions and leave sessions open for extended periods because the human using them moves at human speed. Zero trust, in contrast, collapses both variables at once by narrowing access down to what is strictly necessary and revalidating it continuously, rather than once at login.

"Zero trust really just says, just enough, just in time," Durand says. "It's your next action that we care about. We're moving identity from an era where access was our runtime control point — meaning were you logged in, did you have a session — toward the decision that sits behind that login."

Why agents must be treated as first-class identities

That shift to decision-based control has direct implications for how agents should be provisioned in the first place. The common practice of letting an agent operate under a cloned human login or a shared service account doesn't work, Durand says.

"Each agent should have its own identity," he explains. "It should not be impersonating the human. It can act on behalf of the human, we could explicitly delegate authority to an agent, but we don't want to blur the lines between the human taking action and the agent taking action."

And beyond that is another concern: the shared secrets, API keys in particular, that many service accounts still rely on. For example, the habit of embedding keys directly in source code, where they can be committed accidentally and exposed, is a convenient but weak security pattern that agentic workflows make considerably riskier. Building service account architectures that let agents authenticate without relying on those shared credentials or other long-lived standing access is now an urgent priority rather than a long-term cleanup project.

Where enterprises can enforce zero trust policies

Enforcing any of this in practice requires identifying where policy can actually be applied. Several existing choke points, including API gateways and the agent gateway sitting in front of MCP servers, offer practical locations where enterprises can inspect what an agent is requesting and apply policy rules before granting it.

"Those policies could leverage real-time risk and fraud signals, and then enforce, deterministically, what the agent can do when it interacts with these systems," Durand explains.

The goal is to move authorization from something decided once at login to something evaluated at the moment of every consequential action, such as an agent attempting to commit code to a repository. Instead of carrying a standing permission to write to GitHub, the agent's request would be checked against context and policy at that specific moment, closing the window of trust down to the scope of a single action.

Stopping AI agents from rewriting their own permissions

That model becomes especially important given how agents can behave once they are already inside a system — for example, coding agents that have acknowledged, when questioned, either ignoring a specific guardrail entirely, or attempting to rewrite the permissions they were given.

"Who's watching the watcher? Zero trust needs to apply here," Durand says. "If generative AI systems follow your instruction 97% of the time, and you're simply asking it for advice, that might be fine. If it's responsible for making a decision about who gets let in, 97% is not good enough."

How to trust AI-generated output at agent speed

The answer to that gap is not to eliminate AI from the review process, but to structure reviews so no single agent’s judgment is taken at face value. Because human review cannot scale to the volume and speed of agentic output without erasing the advantage of using agents at all, a new framework is necessary, so that when one agent produces work, such as code, separate agents evaluate it, provided those reviewing agents are kept from communicating with one another or with the one they are checking. It's a new human-AI paradigm, Durand says.

"We probably will have to develop frameworks that we trust without seeing or verifying the output directly," he explains. "It's not that that construct is 100% foolproof. However, it's the best we can do to move at agent speed. We can't trust the exact output, but we can trust the framework."

In practice, that means combining automated review with clear human accountability for higher-risk decisions, rather than treating agent output as self-validating.

For traditional auditors, reviewing every transaction individually is never feasible, and statistically valid sampling stands in for full verification. The same applies to risk accumulation: a single agent action might carry little risk on its own, while a sequence of actions moving in a consistent direction could cross a threshold that triggers an intervention, including a kill switch capable of halting the agent before further harm occurs.

What to ask when evaluating agentic identity platforms

For security leaders evaluating identity platforms for agentic AI, there's no narrow checklist. Enterprises should evaluate what their full lifecycle of agent management looks like. Most enterprises are managing agents on two fronts simultaneously: customer-facing agents acting on behalf of external users, and internal agents deployed to automate enterprise processes.

"Pause long enough to see the totality of what it would mean to secure multiple agents, both interacting with you from the outside as well as being deployed on the inside," Durand says. "We need discovery and visibility of all the agents operating within our estate, a place to register them, a standard way to assign custodians, and a way to construct and centralize policy so security can enforce it across the organization."

And while basic security principles were already fully understood before agentic AI arrived, what has changed, Durand says, is that the cost of moving slowly has finally caught up with the cost of moving carelessly, giving enterprises a narrowing window to build the right architecture before widespread agentic adoption makes retrofitting far more expensive.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust.

This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them.

The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production.

Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education.

At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators.

Finding 1: Confident and wrong

More than half have traced agent errors to bad context

We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had.

This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know.

The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem.

Finding 2: RAG is the default context source

Retrieval feeds more agents than any other method

We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin.

Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface.

One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business.

Finding 3: Provider-native retrieval already leads the vector databases

OpenAI file search and vertex AI search top the dedicated tools

We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists.

The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy.

The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from.

Finding 4: But they say they want to keep best-of-breed

A plurality resist consolidating onto a provider’s native stack

We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage.

Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool.

Finding 5: Hybrid retrieval is the consensus bet

Vector-only retrieval is already seen as insufficient

We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure.

The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed.

Finding 6: The governed context layer is being built now

Most run or are building a semantic layer — few in production

We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived.

The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production.

Finding 7: Bought on ingestion and simplicity, watched for correctness

Selection favors operability; monitoring favors correctness and security

We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical.

Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%).

Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted.

Finding 8: A retrieval reshuffle is coming

A majority plan to change providers — and the vector specialists are gaining interest

We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack.

The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle.

The bottom line: A context gap that more retrieval alone won’t close

Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation.

The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures.

This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop.

The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent.

What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Where questions were multiple-select, those shares can sum to more than 100%.

By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%).

At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators.

Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data.

Finding 1: A passing eval is not a working agent

Half have shipped an agent that passed evals, then failed a customer

We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had.

This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience.

Finding 2: Almost no one fully trusts automated evaluation

The top complaint: Evals don't match real-world outcomes

We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all.

Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking.

Finding 3: The autonomy ceiling is rising anyway

Two-thirds already allow, or are building toward, zero-human deployment

We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap.

Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink.

Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards.  To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that. 

Finding 4: The evaluation stack is fragmented and provider-led

Provider-native evals lead — tied with no dedicated tool at all

We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated.

The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing.

Finding 5: Production monitoring rarely watches output quality

Only a quarter run real-time quality checks on live traffic

Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today.

Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong.

Finding 6: Bought on cost, measured on consistency

Price and integration drive selection; evaluation consistency is the goal

We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic.

Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money.

Finding 7: The next dollar goes to humans and observability

Investment is flowing to oversight, not just automation

We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people.

The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing.

Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make.

Finding 8: A tooling reshuffle is coming

Nearly two-thirds plan to adopt or switch platforms within a year

We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat.

The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint.

Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking.

The bottom line: An evaluation gap that autonomy will widen, not close

Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone.

The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves.

Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries.

Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist 

The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track.

Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled.

Finding 6: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

Finding 7: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

Finding 8: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

Finding 9: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren't yet

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay.

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

The enterprise AI industry has a math problem. Cisco data shows 85% of enterprises are piloting AI agents, but only 5% have shipped them to production. At VB Transform 2026 on Tuesday, Bryan Silverthorn, Director of AGI Autonomy at Amazon, explained why that gap persists — and why the answer isn't better benchmarks.

Silverthorn, who joined Amazon through its acquisition of Adept AI and now leads multimodal agent training inside the company's AGI lab, argued that reliability must be broken into four distinct dimensions: consistency, robustness, predictability, and safety — a framework he credits to research from Princeton.

"It unpacks different factors that I see tangled together in almost every eval I've ever seen," he said.

Why AI agents pass internal evals but fail real customers in production

The framework matters because agents routinely ace internal evaluations and then collapse in the wild. Silverthorn described a customer that deployed an agent for software QA involving serial number extraction from screens. It worked flawlessly for two months — then began intermittently reading wrong numbers. The culprit: the underlying vision encoder behaved differently depending on where the serial number appeared on screen, and a software change imperceptible to humans triggered the failure.

The lesson, Silverthorn said, is about measurement, not just models. "The models have to be better. Obviously, we're working hard on making the models better," he said. But the deeper takeaway, he added, is that teams need to identify their dimensions of variability and match measurement rigor to the stakes of the application. VentureBeat's own proprietary research, presented before the session, reinforces the point: half of surveyed companies shipped agents that passed internal evals but failed real customers, and enterprises overwhelmingly track uptime while ignoring accuracy — checking the pulse without checking the diagnosis. A related finding underscored how few guardrails exist: most enterprises default to the model makers' own evaluations and little else, leaving their testing strategy, as I described it on stage, a coin flip between trusting the vendor and trusting nothing.

Inside Amazon's 'intern' framework for managing autonomous AI agents

Silverthorn's most memorable prescription was cultural, not technical. Inside Amazon's AGI lab, researchers literally call their agents "interns" — as in, "I'll have my intern talk to your intern." The joke carries a serious operational philosophy. Agents, like interns, are powerful but occasionally clueless, capable of amazing work and spectacular derailment.

Managing them, he argued, requires management skills rather than software skills: asking what could go wrong, adding backups and undo capabilities, and consciously deciding what risk you can accept. "You can ask the intern, 'Hey, what might you do wrong here? How might you mitigate your negative outcomes?'" he said. Amazon's lab has embraced that trade-off, accepting agents occasionally running the wrong experiment in exchange for research velocity — including one agent running experiments around the clock on its own high-level research plan.

What enterprise leaders should do before deploying agents at scale

Silverthorn was candid about the limits of today's technology. Self-improving AI remains "a loaded term," he said — Amazon uses AI to improve its models constantly, but fully autonomous self-improvement is distant. Computer use remains a core focus of his lab, with a commercial trucking customer already using browser automation to stitch together warranty claims across fragmented systems**, though he stressed that no future agent will rely on computer use alone — it will work alongside MCP, APIs, and other tools to complete end-to-end workflows**. And LLM-as-judge techniques, while promising, are just one of several strategies for aligning agent capability with acceptable risk.

For enterprises stuck in pilot purgatory, the path forward starts with a mindset shift: stop asking whether your agent can do something impressive once, and start asking whether it can do it correctly a thousand times in a row.

In other words, the enterprises that escape the 85% ceiling won't be the ones with the smartest agents. They'll be the ones with the best managers.

Enterprises looking to move more of their agentic AI workloads to open weights models they can customize, control and run on-premises or in virtual private clouds have a strong new contender to consider.

Today, Thinking Machines—the highly capitalized American AI startup founded by former OpenAI CTO Mira Murati—released Inkling, its first major language model under an enterprise-friendly Apache 2.0 open source license, and it boasts high, if sub state-of-the-art, performance for open weights models on third-party benchmarks, specifically software engineering (77.6% on SWE-bench Verified, where it beats fellow U.S. open rival Nvidia Nemotron 3's 71.9%) and voice understanding (91.4% on VoiceBench compared to 94.4% for Gemini 3.1 Pro on high reasoning effort).

Another differentiator: Thinking Machines notes that Inkling was designed "to answer directly on topics that may be subject to censorship," offering enterprises concerned about factual outputs, irrespective of controversy or sensitivity, a more trustworthy option.

Coming in at 975 billion total parameters, Inkling is a natively multimodal, open-weights Mixture-of-Experts (MoE) system capable of reasoning across text, images, and audio. The weights are already available on Hugging Face and the company's own model training application programming interface (API), Tinker.

Designed to balance cost against performance through a novel "controllable thinking effort" mechanism, the model represents a significant departure from the black-box scaling strategies of frontier competitors.

Alongside the flagship model, Thinking Machines also announced a preview of Inkling-Small, a lighter 276-billion-parameter alternative optimized for workloads where low latency and cost are paramount.

Benchmarks Show a Powerful, High-End, Sub State-of-the-Art Model

While Inkling is a formidable multimodal engine, it lands in a fiercely competitive 2026 open-weight landscape characterized by highly specialized MoE architectures. Rather than attempting to dominate every leaderboard, Thinking Machines explicitly designed Inkling—with 975 billion total and 41 billion active parameters—as a broad, balanced generalist.

For example, it comes in near the middle high-end of benchmark performance 1257 on Design Arena’s Agentic Web Dev leaderboard measuring human scores of frontend web design.

But China’s leading AI labs have produced models with elite reasoning and coding capabilities, posing a stiff challenge to Inkling's generalist approach and ultimately outperforming it on general and coding benchmarks.

• GLM 5.2: Widely considered the top open-weight reasoning model available in the benchmark set, GLM 5.2 outperforms Inkling on pure coding, agentic, and complex reasoning tasks. It scores 62.1% on SWEBench Pro (Public) compared to Inkling’s 54.3%, and a massive 82.7 on Terminal Bench 2.1 against Inkling’s 63.8. GLM 5.2 also holds the edge in text-only reasoning, scoring 40.1% on HLE (text only) versus Inkling's 30.0%.

• DeepSeek V4 Pro: DeepSeek maintains an edge in several strict coding and factuality domains, beating Inkling on SWEBench Verified (80.6% vs. 77.6%) and SimpleQA Verified (57.0% vs. 43.9%). However, Inkling successfully overtakes DeepSeek V4 Pro in mathematical problem-solving, achieving 97.1% on AIME 2026 compared to DeepSeek's 96.7%.

• Kimi K2.6: This model outpaces Inkling across multiple technical benchmarks, delivering higher scores on GPQA Diamond (91.1% vs. 87.9%), BrowseComp (83.2% vs. 77.1%), and HLE with tools (54.0% vs. 46.0%). Yet Inkling proves more resilient on general chat instruction following, scoring 79.8% on IFBench compared to Kimi K2.6's 76.0%.

Against its primary U.S.-based open-weight competition, Inkling demonstrates strong parity and frequent superiority.

• Nemotron 3 Ultra: Inkling consistently outperforms this U.S. rival across reasoning and coding. Inkling posts 97.1% on AIME 2026 and 77.6% on SWEBench Verified, beating Nemotron's 94.2% and 70.7%, respectively. Furthermore, Inkling significantly leads in agentic workflows, scoring 74.1% on MCP Atlas against Nemotron's 44.7%.

When compared to closed-source juggernauts like Claude Fable 5, GPT 5.6 Sol, and Gemini 3.1 Pro, Inkling trails in peak reasoning and software engineering autonomy, but remains highly competitive in multimodality.

• Coding and Reasoning: Closed models maintain a commanding lead. Claude Fable 5 (max) hits 95.0% on SWEBench Verified and 53.3% on HLE (text only), far outpacing Inkling's 77.6% and 30.0%. GPT 5.6 Sol dominates Terminal Bench 2.1 with an 89.5, easily clearing Inkling's 63.8.

• Native Multimodality: Inkling's native visual and audio capabilities hold their own. On the MMMU Pro (Standard 10) vision benchmark, Inkling's 73.3% is competitive, though trailing Claude Fable 5's 84.2% and GPT 5.6 Sol's 83.0%. In audio processing, Inkling scores a highly respectable 77.2% on MMAU, keeping it within striking distance of Gemini 3.1 Pro's 82.5%.

If an enterprise workflow demands elite software engineering autonomy or the highest bounds of text-only reasoning, models like GLM 5.2 or proprietary systems like Claude Fable 5 maintain the edge.

However, Inkling carves out a unique and highly defensible position: it is the most capable open-weight foundation model that natively fuses text, vision, and audio, while simultaneously offering developers direct programmatic control over the cost-to-performance ratio.

The Shift from Static Reasoning to Controllable Thinking

Rather than attempting to build a singular "god model" optimized strictly for state-of-the-art benchmark domination, Thinking Machines engineered Inkling for adaptability and efficiency in real-world workflows.

The standout feature of this release is Inkling's "controllable thinking effort." Developers can programmatically adjust the model's reasoning budget—scaling from 0.2 to 0.99—to dictate how hard the AI should "think" before generating an output.

As the company noted, "Inkling's continuous thinking effort lets you pick your point on the cost/performance curve—reaching the same score with a fraction of the tokens".

In practical terms, this allows enterprises to deploy Inkling with lower token expenditure for simpler tasks, while cranking up the compute overhead for complex, multi-step reasoning challenges. However, by keeping the thinking effort lower and generating fewer tokens, the cost-conscious enterprise can achieve high quality results and performance on simple tasks while spending less money, or, in the case of those running models locally, less costs on energy and compute resources.

During the model’s large-scale reinforcement learning (RL) training over 30 million rollouts, researchers observed an emergent phenomenon they called "chain of thought condensation". Over time, Inkling naturally learned to compress its internal reasoning steps—dropping grammatical overhead and connectives—while reaching the same accurate conclusions, resulting in drastically reduced latency.

Epistemics and Censorship Resistance

A notable element of Thinking Machines' release is its explicit focus on the model's epistemics—specifically its calibration, instruction following, and resistance to censorship.

In an ecosystem where open-weight models adopt either overly restrictive safety guardrails or echo state-aligned ideological talking points, Inkling was intentionally trained to answer directly on politically sensitive or heavily censored topics.

To validate this approach, Thinking Machines submitted Inkling to the Propaganda and Censorship Eval developed by AI startup Cognition. According to the published findings, Inkling demonstrated "strong patterns of censorship non-compliance," effectively resisting ideological capture or boilerplate refusals when presented with sensitive subjects.

Despite its resistance to censorship, the model maintains a robust defense against genuinely malicious, dangerous, or illegal queries. On the StrongREJECT benchmark—which tests responses to unambiguous harmful requests—Inkling scored 98.6%, placing it in line with strict frontier safety standards. Furthermore, on the FORTRESS benchmark, Inkling successfully navigated the line between safety and over-refusal: it achieved a 78.0% refusal rate on adversarial queries (such as those involving weapons, cyberattacks, or violence) while maintaining a 95.9% compliance rate on benign, look-alike queries.

Thinking Machines noted that typical open-weight vulnerabilities remain within the architecture. Internal safety evaluations revealed an "occasional tendency to comply with role-play and indirectly framed prompts concerning harmful topics". The company advised enterprise developers to treat the model's built-in refusals as just one layer of security, recommending the downstream deployment of external moderation tools—such as Llama Guard—to filter adversarial jailbreaks and enforce use-case-specific safety policies at the application level.

Under the Hood: Architecture and Multimodality

Inkling's scale is staggering, yet sparse. The MoE architecture features 975 billion total parameters, but only 41 billion parameters are active during any given token generation. It supports a massive context window of 1 million tokens and diverges from typical transformer models by using relative positional embeddings instead of the industry-standard Rotary Positional Embedding (RoPE).

True to the company's foundational vision, Inkling was trained from scratch to be natively multimodal. Unlike models that rely on bolted-on external encoders, Inkling uses an encoder-free early fusion approach. It directly ingests audio as discrete dMel spectrograms and visual data as 40x40 pixel patches via a hierarchical multi-layer perceptron (hMLP), projecting all modalities into a shared hidden space.

Licensing: True Open-Source for the Enterprise

For enterprise IT teams and developers, the most disruptive aspect of Inkling may be its licensing. Inkling is released under the permissive Apache 2.0 license.

In an ecosystem where many so-called "open" models from Western labs are tethered to dual-use commercial licenses, acceptable use restrictions, or revenue caps, an Apache 2.0 designation makes Inkling a true open-source foundation. This gives developers the legal freedom to download, modify, integrate, and commercialize the model weights entirely royalty-free.

The model is readily deployable across major open-source inference libraries—including SGLang, vLLM, TokenSpeed, and llama.cpp—and comes with a native NVFP4 quantized checkpoint optimized for NVIDIA Blackwell systems.

Community Reactions: The Engineering Feat

The AI community's response has been swift, praising both the model's openness and the underlying engineering execution.

In a post on X, Thinking Machines co-founder John Schulman reflected on the rapid development cycle: "Inkling is out today, with open weights and in Tinker. It's been fun to watch this one come together: pretraining began last winter, and starting in mid-January a small team built up the coding, reasoning, and agentic training from there. We learned a lot building it, and I hope people find good uses for it."

Horace He, a researcher at Thinking Machines (previously from PyTorch), underscored the difficulty of the task in another post on X: "It truly takes a village to release a model, perhaps especially an open weights model. Actually doing the entire process from scratch, from data to pretraining to posttraining to actual release, gives a lot of appreciation for anyone who does it!"

The broader open-source ecosystem has also embraced the technical integrations. Lysandre Debut, the Chief Open-Source Officer at Hugging Face, shared his enthusiasm regarding the model's optimization in his own X post: "One thing I find quite striking is how much easier accelerating models has become... We replaced the model's causal Conv1D with the `causal-conv1d` kernel. One line changed, +4% tokens per second. We then replaced its attention implementation with FlashAttention-4. Another single change, another +11%. That's a total throughput improvement of about 15%, without changing the model architecture or retraining anything."

Tiezhen Wang, an ecosystem growth expert and ex-Googler, celebrated the release as a massive win for the open-source community, listing the model's impressive specifications on X, highlighting its "975B total, 41B active" size, "Native MTP support," and the highly coveted "Apache 2.0 license."

Background: The Road to Inkling

To understand the significance of Inkling, one has to look back at the rapid trajectory of Thinking Machines over the past 18 months.

When Mira Murati departed OpenAI in late 2024 to found Thinking Machines alongside industry veterans like John Schulman and Barret Zoph, the stated goal was to pivot away from building isolated autonomous agents. Instead, the company aimed to build flexible, multimodal systems designed for genuine human-AI collaboration and open science.

By July 2025, the startup had secured a historic $2 billion seed round led by Andreessen Horowitz at a $12 billion valuation. At the time, Murati promised the impending release of a product with a "significant open source component" to empower researchers and startups.

The company’s philosophy began coming into sharper focus in October 2025 with the launch of Tinker, a Python-based API for large language model fine-tuning that gave researchers granular control over training pipelines without the friction of distributed compute management.

That same month, Thinking Machines researcher Rafael Rafailov delivered a provocative critique of the AI industry at TED AI. He argued that the current trajectory of simply throwing more compute at models was fundamentally flawed, noting that today's systems take shortcuts—like wrapping code in try/except blocks—because they are trained strictly for task completion rather than genuine learning.

Rafailov posited that the first artificial superintelligence would not be a "god model," but rather a "superhuman learner" capable of meta-learning and internalizing abstractions. Inkling’s architecture—specifically its controllable thinking effort and its ability to organically compress its chain of thought during RL—feels like the first tangible realization of Rafailov's thesis.

In May 2026, the lab teased its technical prowess with the research preview of TML-Interaction-Small, a system that eliminated "turn-based" chat by processing inputs and outputs simultaneously in 200ms chunks. This "full-duplex" breakthrough proved the company could build highly responsive, natively multimodal models from scratch.

Now, with Inkling out in the wild, Thinking Machines has delivered on its foundational promises. By offering a massive, natively multimodal model under a true open-source license, they aren't just giving developers a new tool—they are attempting to fundamentally rewrite the economics and accessibility of frontier AI development.

Hundreds of enterprise leaders and technical experts packed the main ballroom of the luxurious Hotel Nia in Menlo Park this week for VB Transform 2026, the year's preeminent conference on using generative AI agents to drive business outcomes.

Rachad Alao, vice president of product engineering at the rising Canadian enterprise AI startup Cohere, joined VentureBeat CEO and editor-in-chief Matt Marshall for a fireside chat about building agentic systems without surrendering sensitive data, infrastructure control, or the ability to change vendors.

Alao, who previously led responsible AI and trust and safety engineering teams at Google and Meta, argued that AI sovereignty means more than downloading an open model or running an application behind a corporate firewall.

Asked how Cohere defines sovereignty, Alao pointed to organizations operating mission-critical systems, including banks, hospitals and governments.

“It is important to have very tight control on where the data resides, have tight control on the AI,” he said, adding that AI operations should take place in jurisdictions an organization understands or directly controls.

That extends from GPUs and private-cloud infrastructure through governance systems that route requests among models, as well as the connectors, search tools and agent frameworks acting on enterprise data.

“You want to have control on the entire stack,” Alao said.

Agent workloads could outrun falling token prices

Marshall challenged one of the central economic arguments for smaller, locally deployed models: Inference prices continue to fall rapidly, potentially weakening the case for optimizing every token.

Alao countered that total consumption is climbing even faster as enterprises move from relatively simple chatbots to agents that reason through problems, call tools, search internal systems and take multiple steps before returning an answer.

“Your token utilization is going exponentially up, because you’re dealing with more and more complex agentic use cases,” he said. Those workflows require “a lot of processing, thinking, tools interaction” to complete their objectives, he added.

Alao also drew a contrast between providers that bill customers according to token consumption and Cohere’s approach.

“If your whole way of charging customers is for token utilization, you want to maximize token utilization,” he said. “We do not sell our models and our platform that way.”

Instead, Alao said Cohere tries to help enterprises solve their hardest problems privately and securely while reducing unnecessary model usage. His prescription was straightforward: “Use the right model for the task at hand.”

Rather than sending every request to the largest available frontier model, enterprises should route work according to the intelligence required and the sensitivity or regulatory burden attached to the task.

Alao cited an unnamed Canadian bank that uses Cohere’s on-premises models for highly regulated workloads, while sending less sensitive tasks requiring greater intelligence through Cohere’s North platform to larger frontier models.

“So model routing can become super useful,” he said.

Smaller models for most enterprise work

Asked by an audience member how Cohere’s open-source North Mini Code, released last month, could compete against proprietary coding models, Alao acknowledged that larger frontier models may perform somewhat better on the hardest tasks.

But that advantage may not justify using them indiscriminately.

“For 80% of the use cases that they needed, this was a lot more effective, a lot cheaper,” Alao said of developers adopting the model.

Cohere’s North Mini Code runs on a single Nvidia H100 GPU and targets agentic software engineering, including terminal work, code review and tool use.

The company has also released Command A+, a 218-billion-parameter mixture-of-experts model with only 25 billion parameters active during each generation step.

Its compressed four-bit version reduces the hardware required for private deployment, while its Apache 2.0 license gives enterprises broad freedom to operate and modify it.

Search becomes part of the agent

Asked about Cohere’s longstanding work on embeddings and enterprise search, Alao said the field is moving beyond retrieving text and inserting it into a model’s context window.

“Today, the state of the art is around multimodal search,” he said. “It’s beyond just the text modality.”

Search across documents, images and other forms of information is becoming “an integral component of your agentic workflow,” Alao added, with the model deciding when and how to use retrieval like any other tool.

Asked what would persuade enterprises to move beyond bundled AI services from existing cloud providers, Alao returned to data control and portability.

“If you’re interested in sovereignty, you want to have more control on your data,” he said. Cohere’s governance layer, he added, lets customers route traffic to appropriate models, “breaking that vendor lock-in concern that a lot of our customers have.”

Organizations need to transform to meet the needs of agentic AI.

Meta VP of Engineering Barak Yagour opened his talk at VB Transform 2026 wearing a pair of Ray-Ban Meta AI glasses, a small sign of how far AI has already worked its way into physical life. His argument went further: enterprise infrastructure was built for humans, not for agents, and it's starting to show.

Yagour, who leads its data infrastructure organization, told the audience that agentic queries hitting Meta's data systems grew 30x in a single half, an inversion that he said is breaking assumptions the company spent two decades building around.

The shift is not confined to Meta. Automated traffic overtook human traffic on the internet last year, reaching 51% of the total, according to Imperva's 2025 Bad Bot Report. That traffic is also growing roughly eight times faster than human traffic, according to HUMAN Security's 2026 State of AI Traffic report. Yagour cited both figures to describe what he called an inflection point already underway inside his own organization.

Yagour framed the shift as an open question for infrastructure teams everywhere. "What happens to the infrastructure we've spent years building when agents and not humans become the main consumers of that," Yagour said. "That's the world we're stepping into."

Capacity, identity and velocity are breaking at once

Yagour said three assumptions are breaking simultaneously inside Meta's infrastructure: capacity, identity and velocity.

On capacity, the math no longer works the way engineering teams are used to. "One engineer used to mean one unit of load," he said. "Now one engineer spawns 10 agents, each spawning subagents. Your 1,000-person org can generate the load of 100,000 users practically overnight."

His answer is not to block agent traffic but to make infrastructure agent-aware, with dynamic controls that understand agent hierarchies, cost attribution that traces consumption back to the use case that spawned it, and throttling that adapts based on priority.

Identity is breaking, too. Yagour said an agent does not fit the categories infrastructure teams built access controls around. It is not a human user, it does not carry a badge and it is not a deployed service, yet it makes decisions on its own.

Velocity is the third assumption under strain. Yagour cited a company-reported figure that GitHub Copilot writes 46% of the average user's code, then noted that faster code generation does not make the rest of the pipeline faster.

"That code still needs to be built, tested, deployed, monitored," he said. "The agent writes the code in seconds, but your CI/CD pipeline doesn't get faster just because the machine is the author."

Trusted data environments keep agents inside guardrails

Data is where Yagour said the pressure from agents is most direct. 

"Data sits at the center of everything," he said, pointing to the decisions, products, recommender systems and next generation models it drives.

Meta is also rethinking how much autonomy to grant agents inside its own data systems. In February, the company shipped what Yagour called agentic data apps. Within three months, 63% of dashboards published across Meta were built using the new tooling, part of the same 30x rise in agentic queries Yagour cited earlier.

That growth raises a governance question. Human analysts have traditionally sat between raw data and business decisions, curating it and serving as an informal check on quality. Yagour said Meta wants to grant agents more independence on harder problems, but was direct about the risk. 

"Autonomy without governance is nothing but chaos," he said. That's why the company built what it calls trusted data environments, to preserve the human check as agents take on more of that work.

"Inside, the agent can explore data freely, but every output is traced back to its source and scrutinized. So you always know that the data shared back is trusted and governed," Yagour said.

Sensitive fields are masked before an agent can reach them, and every access request is evaluated in real time against what the agent is trying to reach, why and whether it is allowed. Yagour summarized the approach as exploring broadly while releasing narrowly.

Reasoning models are rewriting the data layer

Meta's models are also demanding more from data as they shift from correlation to reasoning. 

"Reasoning is data hungry," Yagour said. 

Pattern matching works on sparse, summarized signals. Reasoning demands the full behavioral history, every interaction across every surface over time. Yagour pointed to two shifts already underway inside Meta's infrastructure to keep up.

Real-time streaming is replacing batch ETL for ranking pipelines. A pipeline that takes 24 hours to run is not viable when a model is reasoning about a user's current intent. Yagour said real-time streaming, not batch extract-transform-load processing, is becoming the backbone of Meta's ranking and recommendation systems.

Storage is becoming schema-aware to stop GPU starvation. Meta previously stored user data as opaque blobs with no awareness of what the data contained, which Yagour said led to heavy overfetching and idle GPU capacity. The company is now building storage that understands what it holds, pulling only the columns and time ranges a given query needs. Yagour said Meta is building toward 500 million queries per second and a petabyte per second of throughput for training data reads.

That data feeds directly into how Meta's recommendation systems behave. Yagour said 42% of Instagram users have told the company they want to fundamentally change the algorithm, not adjust a single session or setting. Meta's response is what Yagour called fully conversational recommendations, where a user tells the system what they want more of and it reasons about intent rather than matching on keywords. Yagour said the same search term, soccer, would return different results for a casual fan looking for highlights than for a club athlete seeking training drills, because the system would reason about which one is asking.

Yagour described the three threads of his talk, agents, data and recommendations, as reinforcing each other rather than moving independently. 

"Agents make data more accessible. Better data makes reasoning. Reasoning creates new demands that push agents and infrastructure forward," he said. "This isn't linear; it's a flywheel."

During the Q&A, an audience member asked whether Meta's push toward more intelligent infrastructure signals the end of traditional file systems in favor of newer neural storage approaches, and whether agents will keep using SQL as their interface to data the way humans do. Yagour said Meta is experimenting at every level, including questioning whether SQL is the right interface for agents at all, and that storage at Meta's scale already operates in the multi-digit exabyte range and needs to keep expanding.

Yagour closed his talk with the timeline he believes the industry is working against. "We spent 20 years building infrastructure for humans. We have maybe 20 months to rebuild the whole thing for a world where humans and agents co-create at scale," Yagour said. "The window is open, but it won't stay open for long."

1Password on Tuesday launched AI Spend and Consumption Management, a new capability embedded in its SaaS Manager platform that gives IT and finance teams a unified, real-time view of how their organizations consume and spend on AI services from vendors including Anthropic, Cursor, and OpenAI.

The move marks the latest strategic expansion for a company that built its reputation on password management for consumers and, over the past three years, has aggressively repositioned itself as a broader identity security and SaaS governance platform for enterprise buyers. With this release, 1Password is staking a claim in one of enterprise technology's newest and most chaotic budget categories: the consumption-based cost of large language models.

"Executives want teams to build faster with AI, but that speed is creating a new kind of spending pressure," Greg Henry, 1Password's chief financial officer, said in an exclusive interview with VentureBeat. "Developers are consuming tokens at a pace that traditional budgets weren't built to manage, and IT and finance teams are being asked to forecast and justify AI investments without a clear view of what's actually driving costs."

The product, now in public preview with broad availability planned for fall 2026, connects directly to vendor admin APIs to pull token-level consumption data daily. It normalizes that data across providers into a single dashboard and allows organizations to set vendor-level spend limits, configure threshold-based alerts via Slack and email, and break down usage by team, user, vendor, and model.

Why traditional software budgets can't keep up with AI token pricing

The core challenge 1Password is targeting is structural. Traditional SaaS pricing operates on a per-seat, per-year model that is easy to budget and reconcile. AI pricing does not. Every API call to Claude, GPT-5.6, or a Cursor-powered coding assistant consumes tokens, and the cost of those tokens varies by model, by input versus output, and by the complexity of the task. A single engineering team running agentic workflows can burn through a prepaid token budget in weeks — and the finance team may not notice until the invoice arrives.

Henry drew a sharp analogy to a problem enterprises have already lived through once. "Consumption-based pricing isn't new," he said. "We saw it arrive with cloud infrastructure, and it took years to build the tools and disciplines to manage it. AI is the next version of that shift."

That comparison resonates across the industry. When Amazon Web Services, Microsoft Azure, and Google Cloud popularized consumption-based pricing for compute and storage in the 2010s, enterprises initially lacked the tooling to monitor and optimize their cloud bills. That gap spawned an entire FinOps ecosystem — companies like CloudHealth, Spot.io, and Apptio built multi-billion-dollar businesses helping organizations understand what they were spending on cloud and why. Henry is explicitly betting that AI token spend will follow the same trajectory, and that organizations that fail to build visibility now will end up, as he put it, "paying far more than they needed to, for far longer than they should have."

The scale of the coming wave lends credibility to that bet. Goldman Sachs has estimated that token consumption from AI agents alone will grow 24 times by 2030, a projection driven by the expectation that autonomous AI systems will increasingly execute multi-step workflows — booking travel, writing and deploying code, managing customer service interactions — that generate vastly more API calls than a human sitting at a chat interface.

How 1Password's new dashboard tracks every token across Anthropic, Cursor, and OpenAI

The new capability extends 1Password SaaS Manager's existing foundation of application discovery, license management, and spend analytics. It is not a standalone product. Existing SaaS Manager customers can activate it by connecting their supported AI vendor API keys, at which point consumption data flows into a dedicated AI Consumption Management dashboard. Henry confirmed that there is no separate product or add-on fee: "AI Spend and Consumption Management is available to all 1Password SaaS Manager customers."

The system provides four core functions. First, it aggregates token usage and spend across Anthropic, Cursor, and OpenAI into a single, normalized view — eliminating the need to toggle between three separate vendor dashboards with three different reporting formats. Second, it enables budget controls: organizations can set vendor-level spend limits, configure percentage-based thresholds, and receive automated alerts when prepaid balances approach depletion. Third, it disaggregates consumption by team, user, vendor, and model, allowing finance and IT to understand not just how much is being spent, but where and by whom. Fourth, it situates AI spend within the broader SaaS portfolio, helping organizations see how token costs relate to their total software investment.

Notably, the system captures consumption regardless of whether a human or an AI agent generated it. "Token consumption is captured at the API level regardless of whether a human or an agent is generating it," Henry explained. "Organizations get the total consumption picture, including the spikes that agent loops can create, which can be some of the hardest usage to catch before it becomes a problem."

That agent-level visibility matters because autonomous AI systems can generate runaway costs in ways that human users typically cannot. An agentic coding assistant stuck in a retry loop, for example, can consume thousands of dollars in tokens in minutes — with no human in the loop to notice. For now, the product alerts but does not enforce. When asked whether 1Password will eventually give organizations the ability to automatically cut off spending when a threshold is crossed, Henry said the company is "actively evaluating" automatic enforcement but emphasized that visibility must come first: "You can't enforce what you can't see."

The choice of launch partners reveals where enterprise AI budgets are under the most pressure

The decision to start with Anthropic, Cursor, and OpenAI — rather than casting a wider net — reflects where enterprise AI adoption and budget strain are most concentrated right now. Henry said the choice was driven entirely by customer demand. "Anthropic, Cursor, and OpenAI are where we're seeing the highest adoption, and where token consumption can move fast and get ahead of the teams responsible for managing it," he said. The company plans to add additional vendors based on customer demand, API availability, and budget impact, though it has not committed to a specific timeline or vendor list.

The inclusion of Cursor alongside the two major foundation model providers is telling. Cursor, an AI-powered code editor that has rapidly gained traction among developers, represents a category of AI tool where consumption is particularly difficult to forecast. Unlike a chatbot interface where a user consciously types a prompt, Cursor integrates AI suggestions directly into the development workflow, generating token consumption continuously as developers write code. That ambient, always-on consumption pattern makes it especially prone to budget overruns.

Henry also addressed who inside an organization should actually own this problem — and acknowledged that the honest answer right now is no one. "When spend is fragmented across vendor dashboards and finance teams are reconciling it monthly, you're always behind," he said. "AI spend can't be treated as a finance-only or IT-only problem." He noted that the pricing differences between models have become significant enough that the choice of which AI model a team uses is now a meaningful financial decision, one that is pulling CFOs into conversations with IT, product, and engineering leaders "in ways they never had to before."

Steve May, director of IT at ServiceTrade, a 1Password customer that has been using the capability, said it addressed a concrete planning gap. "Forecasting tools for AI consumption and spend was one of our biggest gaps in planning because we didn't have a reliable way to track it," May said. He added that the visibility has "prevented overages that would have cost far more to fix after the fact."

Where 1Password fits in the fast-consolidating SaaS management market

1Password is not the only company racing to solve the AI cost management problem, but the competitive landscape is still fragmented and the category is far from mature.

Zylo, a SaaS management platform that Gartner has also recognized as a leader in the space, published its 2026 SaaS Management Index in January showing that AI-native application spend surged 393% year over year in organizations with more than 10,000 employees and 108% overall. Zylo's data also revealed that ChatGPT has become the most expensed application in enterprise environments, highlighting how AI tools are entering organizations through employee credit cards and expense reports — outside formal procurement and governance workflows. Zylo has added its own token-level cost tracking for AI vendors including Anthropic, OpenAI, Cursor, and Perplexity.

Meanwhile, according to a comparison published by Coommit in May, Vendr — which focuses more on SaaS negotiation than discovery — tracks AI tools at the contract level but does not yet offer consumption-level visibility. And the FinOps Foundation reported in its 2026 State of FinOps survey that 98% of organizations now actively manage AI costs, up from just 31% in 2024. The broader SaaS management market is also consolidating rapidly. In May, Deel acquired Sastrify, a German SaaS management vendor, and began folding it into its HR platform — a signal that SaaS management capabilities are increasingly being absorbed into adjacent enterprise platforms rather than remaining standalone products.

1Password's approach differs from pure-play SaaS management competitors in one important respect: it is building AI cost management on top of an identity security platform, not a FinOps or procurement tool. The company's SaaS Manager product grew out of its 2025 acquisition of Trelica, a UK-based SaaS access management startup whose technology enabled the discovery of unsanctioned applications — so-called shadow IT. As BetaKit reported at the time of that deal, 1Password co-CEO Jeff Shiner described Trelica as "a pioneer in modern SaaS access management" and said the acquisition would accelerate 1Password's Extended Access Management product roadmap by more than a year. CRN noted that Trelica brought more than 300 SaaS integrations to the platform. That identity-first lineage gives 1Password a natural advantage in connecting spend data to specific users and teams — a linkage that matters when the question shifts from "how much are we spending on AI?" to "who is spending it, and is it delivering value?"

From password manager to platform company: 1Password's $6.8 billion bet on enterprise identity

The launch raises a question that Henry addressed head-on: whether a company that started as a consumer password manager can credibly compete in enterprise AI cost management.

"It doesn't feel like a stretch to us. It feels like a natural progression," he said. "For more than 20 years, 1Password has evolved alongside how our customers work. We started by protecting passwords. Then we helped organizations manage secrets, control access, and get visibility into the applications their teams rely on."

The company's evolution has been rapid. 1Password raised a $620 million Series C in January 2022 led by ICONIQ Growth, reaching a $6.8 billion valuation — at the time, the largest funding round ever raised by a Canadian company, according to Crunchbase. The round also attracted celebrity investors including Ryan Reynolds, Scarlett Johansson, and Robert Downey Jr. As of early 2025, BetaKit reported that 1Password had surpassed $250 million in annual recurring revenue, with B2B sales accounting for nearly three-quarters of total revenue and the company claiming to be cash-flow positive.

In May 2024, 1Password launched Extended Access Management, a platform designed to secure sign-ins across both managed and unmanaged applications and devices. That same year, it acquired Kolide for device trust and, in early 2025, Trelica for SaaS discovery. In June 2026, Gartner named 1Password a Leader in its Magic Quadrant for SaaS Management Platforms. According to 1Password's own blog post on the recognition, its SaaS Manager now supports over 400 integrations and provides visibility into a library of more than 40,000 pre-populated application profiles. Each step has moved the company further from its consumer roots and deeper into enterprise infrastructure. The AI Spend and Consumption Management launch extends that trajectory into financial operations territory — a domain where 1Password will compete not only with SaaS management vendors but potentially with dedicated FinOps platforms and the AI vendors' own billing dashboards.

Why high AI token consumption doesn't always mean wasted money

Perhaps the most revealing part of Henry's commentary concerns what organizations should actually do with the consumption data once they have it. He pushed back forcefully against the assumption that high token consumption automatically signals waste.

"A team burning through tokens may be building something genuinely valuable," he said. "A lower-usage project might not be moving the business forward at all. What matters is whether that consumption is producing enough business value to justify the spend."

Henry drew a distinction between personal productivity — "having a bot summarize your meeting or draft a quick email" — and genuine business outcomes. "What organizations need to see is where consumption is actually driving revenue, efficiency, or something that moves the needle."

That framing positions AI Spend and Consumption Management not just as a cost-cutting tool but as a decision-support system for AI investment allocation. If a CFO can see that one engineering team's heavy Claude usage is powering a product feature that drives revenue, while another team's OpenAI spend is funding low-value internal automation, the organization can reallocate budget accordingly rather than imposing across-the-board cuts.

"When costs rise faster than expected, the instinct is to cut," Henry said. "But most organizations can't yet tell which teams, models, or tools are responsible for the increase, so they end up cutting across the board rather than directing investment toward the AI projects that are actually delivering business value. Blunt cuts on a technology you're counting on for competitive advantage is not a management strategy, it's a missed opportunity."

The next enterprise budget crisis is already here — and it's priced per token

The product's current scope — three vendor integrations, alerting but not enforcement — is clearly a starting point. Henry signaled that automatic spend limits are on the roadmap and that additional vendor integrations will follow based on customer demand.

But the broader trajectory he described suggests 1Password sees this launch as a wedge into a much larger opportunity. "As traditional SaaS products add AI capabilities, their pricing models are going to follow," he said. "Organizations that build visibility and management discipline around consumption now are going to be in a much better position when that happens across the rest of their software portfolio."

If Henry is right, the chaos currently confined to AI token budgets is not a temporary growing pain but a preview of how all enterprise software will eventually be priced. A decade ago, companies scrambled to understand their cloud bills. Today, they are scrambling to understand their AI bills. The question is whether the organizations building the dashboards this time around can get ahead of the curve — or whether, as Henry warned, they will end up where so many companies ended up with cloud, realizing too late how much they were overpaying, and for how long.

AI Spend and Consumption Management is available now in public preview for 1Password SaaS Manager customers. Broad availability is planned for fall 2026.

Canva on Tuesday launched Canva Code 2.0, a major upgrade to its AI-powered coding tool that lets users build interactive websites, apps, and experiences using plain-language prompts — and then edit the results as easily as tweaking a Canva presentation. The feature is now available to all of the company's more than 265 million monthly users across every pricing tier, including free accounts.

The move is Canva's most aggressive push yet into the fast-growing "vibe coding" market, a category that barely existed 18 months ago but has already minted billion-dollar startups and reshaped how non-developers think about building software. But where rivals like Lovable, Replit, and Bolt.new have focused primarily on generating functional code from text prompts, Canva is making a different bet: that the real bottleneck isn't creating the code — it's making the output actually look good.

"Most vibe coding tools stop at functional — generating output that looks the same as everyone else's," Canva states in its announcement. "You might get a working prototype, but making it actually look like yours requires a complex editing surface, a separate design tool, a developer, or endless back-and-forth prompting that rarely lands where you want it.”

Danny Wu, Canva's Head of AI Products, framed the product's positioning in stark terms during an exclusive interview with VentureBeat ahead of the launch.

"We are deliberately targeting non-technical users," Wu said. "Canva Code isn't a tool we're building for developers. What we're trying to do is bring the power of AI coding — and really lightweight coding — into the Canva platform, while answering our users' requests for more interactivity, more customization, and more flexibility, from websites to interactive presentations."

Canva Code 2.0 brings drag-and-drop editing, HTML import, and 75% faster generation to AI-built websites

The update introduces several capabilities designed to collapse the distance between generating code and publishing a polished interactive experience. Users can now create Canva Code projects directly inside other design projects — embedding interactive elements within a whiteboard, presentation deck, or standalone page. Canva has also added more than 50 new templates specifically designed for interactive designs, along with the ability to import raw HTML files from other AI coding tools and convert them into editable Canva designs.

The performance improvements are significant. Canva says it has reduced average code generation time by 75 percent and cut the median time from initial prompt to a published site by 30 percent. The company also reports that integrating Canva Code into the broader Canva editor — allowing users to treat coded outputs like any other design element — has increased active Code users by 25 percent.

Perhaps the most distinctive feature is the editing experience itself. Unlike most AI coding platforms, which require users to re-prompt or modify raw code to make visual changes, Canva Code 2.0 lets users click directly into generated elements to change text, drag and drop images from Canva's built-in library of over 120 million templates and assets, update colors and fonts through a familiar toolbar, or select a specific element and refine it through conversational AI. Every output is fully interactive and automatically adapts to different screen sizes, with a built-in mobile preview.

Wu demonstrated the drag-and-drop editing during the interview, showing how a generated conference website could be modified in real time — swapping in photos, changing fonts to branded alternatives, and editing text directly on the canvas. "The key differentiator with Canva Code is the editability and the kindness of the outputs it generates," he said, though he noted one current limitation: "We don't support moving elements around. You still have to re-prompt for that."

How Canva plans to compete with Lovable, Replit, and Bolt in the booming AI app builder market

Canva's entry into vibe coding at this scale arrives at a pivotal moment for the category. According to market research published by Luminix AI in May 2026, the vibe coding and AI app builder market has reached an estimated $4.7 billion in 2026, with projections pointing toward $12.3 billion by 2027 at roughly 38 percent compound annual growth. The research also estimates that AI-generated code now comprises approximately 41 percent of all code written globally — a figure that would have seemed inconceivable even two years ago.

The competitive landscape has grown ferocious. Lovable, which focuses on conversational, design-forward app generation for non-technical founders, has achieved what may be the fastest revenue ramp in the category's history — reportedly reaching approximately $400 million in annual recurring revenue by early 2026, according to Luminix's analysis. Replit, which transformed its browser-based IDE into a full vibe-coding engine through successive AI agent releases, has tripled its valuation to $9 billion and is targeting $1 billion in run-rate revenue by the end of 2026, per the same report. Bolt.new, which runs a full Node.js environment entirely in the browser, scaled from $4 million to $40 million in ARR within months of launching.

And then there is Canva, which brings something none of those platforms possess: a quarter-billion-user design ecosystem where brands, teams, and individuals already store their visual identities, collaborate on projects, and publish content.

Wu positioned Canva Code not as a direct competitor to these developer-focused tools but as something that fills a gap none of them have addressed. "A lot of the requests that we have been getting and the usage we're seeing is actually with using Canva Code not necessarily as just one artifact, but as part of an overall design, the visual communication they're trying to tell," Wu said. "Like when you have a sales deck, you're able to add a calculator, you're able to add a visualizer of what exactly your product does. That's something where an interactive slide can be worth a thousand pictures."

Why Canva's HTML import feature could turn it into a 'finishing layer' for every AI coding tool

One of the most strategically interesting features in Canva Code 2.0 is its HTML import capability, which allows users to take code generated by any AI tool — including ChatGPT, Claude, Lovable, or Bolt — and bring it into Canva as a fully editable design. The implication is unmistakable: Canva is positioning itself as the place where AI-generated code gets its finishing touches, regardless of where it was originally created.

When asked directly whether this amounts to positioning Canva as a "finishing layer on top of vibe coding," Wu offered a diplomatic but revealing response. "It's really a continuation of our goal to make all design as easy as possible," he said. "We've supported importing PDFs and translating them into docs, importing PowerPoint files — so in one way, it's an expansion of that. But in another way, it's really just listening to what our users want and making Canva both the most useful and the most compatible platform.”

He paused, then added: "It's not that we're deliberately positioning ourselves as a specific layer, say like a finishing layer after vibe coding. We just really want to make our platform the most accessible and the most pluggable."

That language — "most pluggable" — suggests a platform strategy that doesn't require Canva to win the AI code generation race outright. If Canva becomes the default destination for making AI-generated code look professional and on-brand, it captures value from the entire category regardless of which code generation engine users prefer. The strategy also echoes the broader import capabilities that already allow Canva to ingest PowerPoint decks and PDFs from competing platforms, gradually pulling users deeper into the Canva ecosystem without demanding they abandon existing workflows.

What Canva Code can build — and where Danny Wu says it hits its limits

Wu was notably candid about the product's boundaries — a refreshing departure from the typical Silicon Valley product launch. "Canva Code is great for anything that works as a front-end app, and it's especially good when you want to leverage data, data submissions, and interactivity at small to medium scale," he said. "I'll be honest about the limitations. Canva Code is probably not going to be suitable if you're trying to build a website with complex backends, or if you're handling hundreds of thousands of visitors per day."

This candor effectively draws a line between Canva Code and the more ambitious platforms in the space. While Lovable and Replit are pushing toward full-stack application development — complete with databases, authentication, and production-grade hosting — Canva is deliberately limiting its scope to interactive front-end experiences at modest scale. The question is whether that's a strategic weakness or a disciplined focus. For the teachers, small business owners, and marketing teams that make up the bulk of Canva's user base, complex backends and high-traffic scalability are irrelevant concerns. What matters is whether they can create an interactive event page, a property listing website, or a classroom hub that looks professional and works on mobile — without hiring a developer or learning a new tool.

When asked about the AI models powering Canva Code, Wu confirmed the company uses a combination of proprietary and third-party models, including those from OpenAI and Anthropic, but declined to specify the exact mix. "We don't share the exact mix, and it does change over time," he said. "We also route differently depending on what you're asking for and which model family we think is best for handling certain requests."

Canva's AI acquisition spree — from Affinity to Leonardo.ai — now powers its vibe coding push

Canva's broader AI infrastructure has been significantly bolstered by an acquisition strategy that has accelerated over the past two years. In March 2024, the company acquired Affinity, the British creative software suite popular with Mac users, in a deal that Bloomberg reported was valued at "several hundred million pounds." Canva at the time positioned the deal as a way to compete with Adobe's flagship products — Illustrator, Photoshop, and InDesign — by gaining ownership of Affinity's Designer, Photo, and Publisher applications.

Just four months later, Canva acquired Leonardo.ai, an Australian generative AI startup with over 19 million registered users and more than a billion images generated. Canva co-founder Cameron Adams said at the time that Leonardo.ai's technology would be integrated into Canva's Magic Studio generative AI suite.

Together with these acquisitions, Canva Code is the company's attempt to layer interactive, code-driven capabilities on top of a visual design platform that has already been enhanced by professional-grade design tools and generative AI models. The company reports over 32 billion uses of its AI products to date — a staggering figure that underscores how deeply AI is now woven into everyday Canva workflows, even for users who may not think of themselves as using artificial intelligence.

Six million sites published, but Canva's retention data remains an open question

Canva's announcement highlights an impressive traction metric: users have created and published more than six million websites using Canva Code since the feature was first introduced a year ago. But the number deserves scrutiny.

Wu clarified in the interview that the six million figure represents published websites over the past year — meaning sites that were either made public or shared via password-protected or private links. "They may have published publicly, or behind a password, or as a private link. But that's the number of published websites," he said.

When asked about active retention — how many of those sites are still live and being maintained — Wu acknowledged the gap in his data. This is a meaningful distinction. In the vibe coding market, raw creation numbers can be misleading because the barrier to generating a site is so low. The more telling metric — which Canva does not yet provide — would be how many of those six million sites receive regular traffic or have been updated after initial publication.

The early use cases, however, suggest genuine utility beyond novelty. Educators and school administrators are using Canva Code to build classroom hubs, with one teacher creating bespoke webpages for each of their classrooms to keep students and parents updated on announcements. Small businesses, like Alt Marketing School, have built mini apps for fundraising training and interactive roadmaps for their members. For World Book Day, 50 readers created educational games across different subjects, complete with pedagogical guides for classroom use.

Canva Code pricing, data governance, and what enterprise customers need to know

Canva Code 2.0 is available across all of Canva's pricing tiers, including its free plan — a notable decision given that competitors like Lovable, Bolt, and Replit reserve their most capable features for paid subscribers. "As you go from, say, free to pro to business to enterprise, you would get more AI credits and be able to have higher usage of Canva Code," Wu said. "But it is available and it is usable — even free Canva accounts as well as education and not-for-profit accounts."

This credit-based approach mirrors the pricing evolution happening across the entire vibe coding category, where platforms have converged on token or credit systems that meter AI generation capacity rather than gating features behind subscription tiers. The difference is that Canva's free tier serves as an acquisition funnel for a much larger design platform, not just for the coding feature itself.

For the institutional customers Canva increasingly courts — school districts, real estate brokerages, enterprise marketing teams — data governance is a threshold concern. Wu addressed this directly. "All users and customers have full control over how their data is used," he said. "They can choose whether their prompts and data are used for AI training in the settings. For businesses and enterprises, team admins can manage this at the organizational level and guarantee that their inputs, content, and outputs won't be used for training." This opt-out approach reflects a lesson the broader industry has learned the hard way. As The Verge reported when Canva acquired Leonardo.ai, Adobe suffered significant backlash over a policy update regarding user data and AI model training — a controversy Canva appears keen to avoid.

Canva's long-term vision: closing the gap between imagination and what non-technical users can actually build

When asked where Canva Code fits into the company's long-term trajectory — and whether Canva is building toward a full-stack app development platform — Wu steered the conversation back to the company's core audience.

"A huge part of it is reducing the gap between your imagination and what's possible, especially for everyday users — people who don't have a lot of time," he said. "They don't have time to figure out deploys or MCPs or APIs. They just want to design more interactive and more dynamic communication."

He pointed to the rapid improvement in AI model capabilities as a key accelerant. "The kind of things you can create today in one shot — like a 3D visualization of a solar system — you really couldn't have trusted the output a year ago. But today, you have a really high success rate."

Whether Canva Code becomes a durable product category or a feature that gets absorbed into the platform's broader AI workflow will depend on how quickly the company can close the gap between its current front-end focus and the full-stack capabilities that increasingly define the competition. Lovable is shipping Supabase-backed apps with authentication and databases built in. Replit's agents can execute autonomous long-running builds. Bolt.new runs entire Node.js environments in a browser tab. These are fundamentally different ambitions than making a conference landing page look good.

But Canva has never won by matching the technical depth of its competitors. A decade ago, it didn't try to out-feature Adobe — it made design accessible to the 99 percent of people who would never open Photoshop. Now, in a vibe coding market where every tool can generate a working prototype from a prompt, Canva is making the same wager it made in 2012: that for most people, the hardest part was never the building. It was making it look like it came from you.

Model routing is becoming a key component of the enterprise AI stack, dynamically sending prompts to the right AI model to optimize speed and costs. However, current frameworks mostly treat routing as a static classification problem, which severely limits their potential.

A new open-source framework called Agent-as-a-Router tackles this bottleneck, treating the router as a dynamic, memory-building agent. It uses a Context-Action-Feedback (C-A-F) loop to track model successes and failures and update the behavior of the router. 

The researchers also released ACRouter, a concrete implementation of this paradigm. In their tests, ACRouter significantly outperformed static routers and the expensive strategy of defaulting to premium models, all without requiring teams to train massive models or write endless heuristics.

For real-world applications, this framework provides the option to replace hard-coded AI infrastructure with self-optimizing systems that can adapt to changes in user behavior and foundation models used in the enterprise AI stack. 

The economics of routing and the information deficit

Single-model setups are useful for experiments but detrimental when scaling AI applications. AI engineers use model routing to map tasks to cheaper and faster open models when possible, while reserving expensive frontier models for complex reasoning. 

Currently, developers rely on two main mechanisms for this task. The first is heuristics-based routing, which relies on hard-coded manual rules. For example, a developer might write a rule dictating that if a prompt contains certain keywords, it is routed to GPT-5.5. Otherwise, it goes to a self-hosted open source model like Kimi K2.7. 

The second mechanism is static trained policies. These are machine learning classifiers trained on historical datasets that look at the prompt's embeddings and predict the best model based on past training data.

Both approaches are static. When the researchers tested these existing mechanisms on real-world coding and agentic workflows, they found a hard ceiling on accuracy. The key finding shows that static routers suffer from a severe information deficit. Because they only evaluate the input text and never see if the model actually succeeded in executing the task, they guess blindly when faced with complex edge cases.

This results in three distinct points of failure. First, static routers suffer from a frozen information state, meaning they cannot accumulate new execution feedback during deployment. Second, they fail in out-of-distribution (OOD) generalization. They break down during day-two operations when enterprise data or user behavior shifts because their training data no longer matches reality. Finally, they are highly vulnerable to model churn. A static classifier trained on today's models may become obsolete when a better model drops the following week.

Agent-as-a-Router: A self-evolving system

The core thesis of the Agent-as-a-Router is that a truly effective router must acquire and accumulate execution-grounded information during deployment, essentially learning on the job. 

The researchers achieved this through the C-A-F loop. When a new prompt arrives, the router examines the prompt and task metadata, such as the programming language or difficulty. It then searches its historical memory for similar tasks to see which models succeeded or failed in the past. The router uses this context to select the target model and execute the task. Finally, the system observes the real-world outcome, extracts a success or failure signal, and writes this feedback back into its memory to inform future routing decisions.

Consider an automated enterprise data analytics pipeline. The router receives a SQL generation task and sends it to an open-source model like Kimi. The model hallucinates a column name and fails to compile the SQL. The C-A-F loop observes the compiler error, registers it as feedback, and logs it. The next time a similar obscure SQL query arrives, the router checks its context and routes the task to a more advanced model like Claude Opus 4.8. 

ACRouter

The researchers developed ACRouter as the concrete instantiation of this framework. It is composed of three core components: the Orchestrator, the Verifier, and Memory. This architecture is supported by a tool layer to physically execute the C-A-F loop.

The Memory module powers the context phase. Built on a vector store, it retrieves relevant past interactions and updates the historical database with new outcomes. The Orchestrator handles the action phase. It processes the user prompt alongside the retrieved memory to select the most capable target model from the available pool. The Verifier manages the feedback phase by evaluating the chosen model's output to generate a clear success or failure signal.

The tool layer hooks the Verifier into real-world execution environments, like a Python code interpreter, an agentic sandbox, or a database engine. The tool layer allows the system to execute the generated code or query and observe the exact outcome, providing the verifiable signal the router needs to learn.

The Orchestrator itself is lightweight. Instead of a massive, computationally heavy large language model, the researchers trained a sub-billion parameter adapter based on Qwen 3.5 (0.8B parameters), which means it can be self-hosted on a device of your choice.

ACRouter in action: Outperforming the frontier baselines

To stress-test the framework, the researchers introduced CodeRouterBench, an evaluation environment comprising roughly 10,000 tasks with verified scores across eight frontier models, including Claude Opus 4.6, GPT-5.4, Qwen3-Max, and GLM-5. The evaluation was split between in-distribution (ID) tests (covering nine single-turn coding dimensions like algorithm design and test generation) and an out-of-distribution (OOD) agentic programming testbed. The OOD tasks were qualitatively different, requiring multi-step planning, file navigation, and iterative debugging to see if the router could adapt to fundamentally new domains.

The baseline results revealed why a single-model strategy is flawed: no single model dominates every category. For example, while Claude Opus 4.6 achieved the highest average performance, it was outperformed in algorithm design by GLM-5 (an 86% relative improvement) and in test generation by Qwen3-Max (a 111% improvement), despite Opus costing roughly 12 times as much as smaller models like Kimi-K2.5. 

In the benchmarks, static routers continuously failed by sending a specific niche coding task to a model ill-equipped for that exact syntax. The static router had no way to know the code was failing to execute. In contrast, ACRouter adjusted its strategy after receiving negative feedback signal from the execution environment. 

According to the researchers' benchmarking, ACRouter sits firmly at the Pareto frontier of cost and performance. On both the ID task streams and the complex OOD agentic tests, ACRouter achieved the lowest cumulative regret, a metric measuring sub-optimal routing decisions over time. On the in-distribution test set, ACRouter cost $13.21 across the full task run, compared to $34.02 for always defaulting to Opus — a 2.6x savings.

It dynamically matched tasks to the most capable model for that specific niche, suggesting that enterprises can achieve or exceed frontier-level accuracy across diverse workloads without paying a premium price for every query. 

Caveats, limitations, and how to get started

While the Agent-as-a-Router paradigm solves the information deficit, it is not a blanket solution for all AI workflows. 

The framework shines in verifiable tasks where the Verifier gets a clear success or failure signal from the environment, such as coding or data retrieval. It is effective for applications with distribution shifts and domains where different models excel in completely distinct niches. 

Conversely, the setup is overkill for trivial tasks where any model will suffice, or for low-volume applications that do not justify the engineering overhead. It is also unsuitable for subjective domains, such as creative writing, where a correct answer cannot be easily verified and feedback signals are impossible to standardize.

The researchers open-sourced the code on GitHub and released the orchestrator model weights on Hugging Face under the Apache 2.0 license. The router is compatible with Claude Code, Codex, and OpenCode.

Presented by Kasm Technologies

Enterprise infrastructure teams have spent the better part of a decade pushing workloads into Kubernetes. Applications, APIs, batch jobs, data pipelines — if it runs in a container, it belongs in the cluster. The operational benefits are well-established: declarative configuration, horizontal scaling, self-healing, native integration with CI/CD pipelines and observability tooling. Kubernetes has become the default operating model for production workloads.

Except for desktops.

Secure desktop and application delivery — the kind that enterprises depend on for remote work, privileged access, and regulated-industry workflows — has remained stubbornly outside the Kubernetes model. Legacy virtual desktop infrastructure was built in a different era, for a different set of assumptions: pre-allocated VM pools, bespoke management planes, proprietary appliances, and operational tooling that has nothing to do with how modern platform teams work. The result is a split infrastructure reality: a modern, cloud-native application layer on one side, and a manually managed, operationally isolated desktop layer on the other.

That split is expensive. It means different tooling, different scaling behaviors, different observability approaches, and different operational runbooks. Platform engineers who are proficient in Kubernetes still have to context-switch into an entirely different mental model the moment a desktop infrastructure problem arises.

The more fundamental issue is that this split is unnecessary. Secure, containerized workspace delivery is a workload that Kubernetes is architecturally well-suited to run. Sessions are containers. Scaling is demand-driven. Configuration should be declarative. The only thing missing was a platform built to take advantage of that alignment.

Why the timing is right

The appetite for Kubernetes-native workspace delivery has grown significantly as organizations mature their container platform investments. Platform teams that have spent years standardizing on Helm, GitOps workflows, and Kubernetes-native observability are increasingly unwilling to make an exception for desktop infrastructure. The question has shifted from "can we run this on Kubernetes?" to "why isn't this running on Kubernetes already?"

At the same time, the security case for containerized workspace delivery has become more urgent. Browser-delivered, containerized workspaces provide session isolation that VM-based desktops cannot match — each session is ephemeral, isolated at the container boundary, and terminates cleanly without persistent state. For organizations managing sensitive data, insider risk, or third-party access scenarios, this isolation model is a meaningful security control, not just a deployment convenience.

The convergence of these two trends — Kubernetes-native infrastructure expectations and containerized session security — creates a clear opportunity for platforms that can address both simultaneously.

What Kubernetes-native deployment looks like

A Kubernetes-native deployment uses Kubernetes as the control plane for workspace infrastructure — handling orchestration, scaling, and lifecycle management through the same declarative model used across the rest of the platform. Instead of relying on dedicated management appliances or pre-provisioned desktop pools, infrastructure is managed through the same CI/CD, GitOps, observability, and security workflows the platform team already operates. This gives platform teams a consistent operational model rather than maintaining a separate toolset for desktop infrastructure.

Kasm Workspaces, the browser-delivered workspace platform, is purpose-built to use Kubernetes as the control plane for workspace orchestration and delivery. Its deployment model is designed for real enterprise environments — not simplified demos — with production-grade Helm charts that follow Kubernetes conventions, tested upgrade paths between versions, and a standardized backend architecture validated across production deployments. An RDP Gateway component purpose-built for the Kubernetes topology enables Windows and Linux virtual machine access through the same platform.

Key capabilities include:

• Horizontal session scaling driven by actual demand, orchestrated by Kubernetes — no pre-warmed VM pools required.

• Declarative configuration through Helm values, enabling GitOps and CI/CD integration for workspace infrastructure.

• Namespace-level isolation and compatibility with existing RBAC policies, ingress controllers, and secrets management integrations.

• Metrics export for integration with Prometheus and existing observability stacks.

• Rolling builds by default, reducing maintenance windows and enabling more predictable version management.

Real-world applications

Regulated-industry remote access. A financial services organization running a Kubernetes-based application platform can deploy Kasm into the same cluster, using the same operational tooling, to deliver isolated browser and application sessions to analysts and advisors. Sessions are ephemeral, network egress is controlled, and the entire deployment is managed through the same GitOps pipeline as their application workloads.

Contractor and third-party access. Organizations that regularly onboard contractors or external vendors — with the associated privileged access risk — can provision Kasm sessions on Kubernetes that scale up during engagement periods and scale back during low-demand windows. No persistent access. No VPN extension to external parties. Containerized isolation at every session boundary.

AI/ML development environments. Teams building and running AI models need GPU-enabled development environments with security controls that general-purpose cloud desktops rarely provide. Deploying Kasm on Kubernetes with NVIDIA MiG Multi-Instance GPU support lets platform teams deliver fractional GPU resources into isolated workspace sessions — giving data scientists the compute they need without shared-infrastructure security exposure.

The operational shift

The practical implication of a Kubernetes-native workspace platform is that platform teams can stop treating workspace infrastructure as a special case. The same engineers who deploy applications can deploy the workspace platform. The same pipelines that manage application configuration can manage workspace configuration. The same dashboards that monitor application health can monitor workspace health.

That operational consolidation reduces overhead, improves consistency, and eliminates the context-switching cost that has made desktop infrastructure a persistent pain point for cloud-native organizations.

For organizations still running legacy VDI alongside modern cloud infrastructure, the question is no longer whether a Kubernetes-native alternative exists. It does. The question is when to make the transition.

Organizations interested in evaluating Kubernetes-native workspace delivery can explore the platform at kasm.com and try out community edition for yourself.

Daniel Ben-Chitrit is the Chief Product Officer at Kasm Technologies.

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