Halfway through 2026, artificial intelligence has been at the center of every major story inside the world of software development and in just about every major story outside of it.

Last month, the Commerce Department ordered Anthropic to pull Fable 5 and Mythos 5 offline worldwide — only to lift the ban 18 days later. It wasn’t Anthropic’s only government clash; earlier in the year, the Pentagon fought the company over its refusal to give the military unrestricted model access.

Elsewhere, frontier labs planted their flag on Wall Street in May with new deployment arms and partnerships, while Anthropic and OpenAI both pursued IPOs at valuations above $800 billion. Underneath the valuations is an infrastructure buildout of chips, data centers, and deals meant to keep pace with model releases that land every few weeks.

Meanwhile, open-weight models are narrowing the gap between closed and downloadable, and the harness — the tools, memory, and orchestration around a model — matters more as agentic AI moves into the enterprise. Add tokenomics (the real cost of reasoning at scale) and non-technical execs vibe-coding their own tools, and you’ve got ten of the biggest AI moments of 2026 — so far.

Here are ten moments that have defined a pivotal first half of 2026 in the world of AI, as chosen by the editorial staff at The New Stack.

10. President Trump’s Executive Order on AI

President Donald Trump has been relatively friendly to AI, given his support from tech companies in Silicon Valley. On June 2, 2026, Trump signed an executive order aimed at hardening American systems against AI- related threats, while at the same time rejecting “overly burdensome regulation.” The order directs the Committee on National Systems Security to prioritize cyber defense and tasks the Treasury, the NSA, and CISA with establishing an AI security clearinghouse to coordinate vulnerability scanning and patching across critical infrastructure. The administration supports deregulation along with national security-driven AI oversight.

9. AI infrastructure buildout

Chipmakers and AI labs tightened ties in 2026 to keep pace with model releases. We also saw big moves, with Nvidia and SK Hynix striking a multi-year partnership spanning Vera Rubin supercomputers, Vera CPUs, and next-gen memory. Meanwhile, data center capacity has expanded globally. The buildout shows that compute, power, and hardware are now a bottleneck to AI’s growth.

8. The rise of the harness

“The harness is where the hard work is,” Harness CEO and founder Jyoti Bansal told The New Stack last month. As base models are performing closely on benchmarks, the harness becomes the differentiator. The harness is the tools, memory, orchestration, and guardrails wrapped around a model. The harness determines whether an agent stays on task, recovers from errors, and performs safely. The harness helps shift competitive advantage from model capability to system design.

7. Tokenomics

Spend is now the battleground for both AI producers and consumers. AI labs are restructuring pricing around compute consumption rather than flat subscription, and companies are looking for ways to cut their token spend. Last month, the Linux Foundation launched the Tokenomics Foundation with support from Google, Microsoft, IBM, JPMorgan Chase, KPMG, Oracle, and Salesforce. The organization is tasked with establishing open standards, benchmarks, and best practices across the entire AI token economy.

6. Agentic AI goes mainstream

A year ago, agents were largely proof-of-concept demos that were unreliable in production. In 2026, they became infrastructure. For instance, ChatGPT’s browsing agent, Claude’s tool use and multistep coding runs, and Google’s autonomous information agents now run continuously in the background rather than on command. Meanwhile, enterprises are adding agents into real workflows such as monitoring, code review, procurement, and customer support. However, this shift may carry security risks as agents gain access to data and systems.

5. The Pentagon goes to war with Anthropic

In February, Department of War Secretary Pete Hegseth summoned Anthropic CEO Dario Amodei to his office to demand that the military be granted unrestricted use of the company’s technology. But Amodei held his ground and refused to allow the military to use Anthropic technology for mass surveillance on citizens or for autonomous weapons. Following that, President Trump ordered federal agencies to phase out the use of Anthropic, and Hegseth designated the company a “supply chain risk,” which is a label previously used for foreign adversaries – basically blocking the company. Anthropic sued in federal court, claiming the government’s move was unfounded and retaliatory. The company received a preliminary injunction from a San Francisco court, holding that the government’s actions constituted unconstitutional First Amendment retaliation. 

Meanwhile, amid the initial fallout from Anthropic’s battle with the Pentagon, OpenAI made its own deal with the military.

4. AI titans plant their flags on Wall Street

Within 72 hours in May, Anthropic and OpenAI each launched enterprise deployment arms, announced major financial services partnerships, and shipped agent tooling targeting Wall Street workflows. The message was the same — the next phase of frontier AI is not about models. It’s about deployment. 

Anthropic’s new services firm — backed by Blackstone and Hellman & Friedman alongside General Atlantic, Apollo, Goldman Sachs, and Sequoia Capital — targets mid-sized enterprises that the large consulting and systems integration firms don’t prioritize. These include community banks, regional health systems, and mid-market manufacturers. Applied AI engineers from Anthropic embed directly with clients alongside the new firm’s own engineering staff, doing workflow discovery, building custom Claude-powered solutions, and supporting clients long-term.

OpenAI’s Deployment Company — “DeployCo” — operates one market segment up, targeting large enterprises with the same forward-deployed engineering model. Its acquisition of applied AI consulting firm Tomoro brings roughly 150 experienced Forward Deployed Engineers (FDEs) from day one, backed by more than $4 billion in initial investment and a partner roster that includes McKinsey, Bain & Company, and Capgemini.

Meanwhile, both companies are considering IPOs with valuations exceeding $800 billion.

And both companies are betting on the same thesis: that the deployment gap — the widening distance between what frontier AI can do and what enterprises have actually shipped — is the next major revenue opportunity. And both moved on it in the same week. 

3. Open-weight models are coming

Chinese labs continued to close the gap with Western frontier labs in 2026. Alibaba’s Qwen, Zai’s GLM and Moonshot’s Kimi, delivering open-weight releases that rivaled closed models on standard benchmarks. Zai’s GLM-5.2, released June 13, beat Anthropic’s Claude Opus 4.8 in some benchmarks – showing the highest marks for open-weight models. GLM-5,2 is also one-fifth the price of comparable closed models. 

“The industry is all focused on which lab has the smartest model, but that focus looks to the past,” David Mytton, CEO of Arcjet, told The New Stack. “GLM-5.2’s capability indicates that usage of open-source models is about to explode. “This all seems obvious in retrospect: models became more capable at the start of the year, agents started taking real actions (particularly since most of the work happens following the chat prompt), and legal restrictions on using frontier models are causing people to look elsewhere. This will cause all sorts of security issues because managing so many model capabilities will become difficult.”

Paul Sawers, contributing writer at The New Stack, tells us for this countdown: “Budget open-weight model panels are now matching frontier proprietary benchmarks at a fraction of the cost, undercutting the case for paying top dollar for a single closed model. Example of startup ditching Anthropic for DeepSeek.”

Indeed, the European AI agent startup Lindy AI migrated 100% of its production traffic from Anthropic to DeepSeek, citing millions of dollars in savings,

2. CEOs vibe coding their own tools

While software developers seem to have wholly adopted AI coding assistants, the business side of organizations — the C-suite and other executives high up in the chain of command — is adopting these tools to “vibe code” a variety of agents and productivity applications.

The trend runs from simple workflow automations to full production systems serving hundreds of users. The tools are Claude, Cursor, and, increasingly, the AI features embedded in the platforms these executives already run. The motivations range from impatience with IT queues to genuine curiosity about what the technology can do. And the results are more varied than the enthusiasm surrounding them might suggest.

Woodson Martin, CEO of OutSystems, took a more structured approach to his own vibe coding experiment. He built a personal mobile app wrapper on top of MCP services his team had created — and he built it twice in parallel, once using OutSystems’ own AI coding tool, Mentor, and once using Claude, connecting to the same backend both times.

“I was tired of explaining it to somebody who was supposed to build it for me,” Martin told The New Stack in April. “I was just like, ‘I’ll do this myself.’”

The app is a personal chief-of-staff system that consolidates customer account intelligence — buying signals, website activity, internal data — into a pre-meeting briefing he can pull up on his phone. It replaced what had been a 45-minute PowerPoint session plus multiple prep meetings from his sales team. 

1. The government cracks down (and later relents) on Anthropic Fable 5 and Mythos 5

The Fable/Mythos takedown illustrated how unpredictable AI policy has become. Anthropic launched Fable 5, its only Mythos-tier model, on Jun 9, 2026, with the fuller Mythos 5 reserved for a small set of trusted customers under what the company called Project Glasswing. The rollout lasted only three days.

On June 12, Commerce Secretary Howard Lutnick sent Anthropic CEO Dario Amodei a directive ordering the immediate worldwide suspension of both models for all foreign nationals, including Anthropic’s own non-citizen employees.

Apparently, the trigger was a jailbreak that Amazon researchers found, which could expose the models’ cybersecurity capabilities. This raised concerns at Commerce. Anthropic said it didn’t have a way to restrict access by nationality in real time, so it disabled Fable 5 and Mythos 5 globally. The Commerce Department partially opened Mythos 5 to select government-approved organizations in the following weeks, but the freeze didn’t lift until June 30 — after an 18-day period of limbo.

Meanwhile, Anthropic added extra cybersecurity safeguards to Fable 5 and began restoring global access on July 1.

Having mentioned Anthropic’s run-in with the Pentagon, Frederic Lardinois of The New Stack tells us: “While the two situations are not directly linked, it’s hard not to read the Fable controls as an extension of this existing animosity between Anthropic and the Trump administration — and in part, this seems personal as well.”

What’s next

What’s in store for the second half of 2026? We’ll be tracking how much AI-generated code actually makes it into production — and the tools designed to close that gap; the expanding autonomy of agentic AI and the guardrails keeping pace with it; the fast-moving regulatory environment around the frontier labs; and enterprise adoption of open-weight models.

We’re also watching AI’s spread among knowledge workers, the ever-longer stretches agents run unsupervised, and the hiring and performance of forward deployed engineers.

Whatever happens this year, The New Stack will cover it. And if you haven’t already, subscribe to The New Stack Daily newsletter for timely, thoughtful updates on how AI is reshaping software development.

The post 10 moments that defined AI’s turbulent first half of 2026 appeared first on The New Stack.

Okta has made its AI agent governance platform generally available for FedRAMP- and HIPAA-regulated environments, becoming what it claims is the first independent identity platform to extend AI agent lifecycle management inside the compliance boundaries federal agencies and healthcare organizations already trust.

The product, Okta for AI Agents – Core, elevates AI agents to first-class identities managed alongside human and machine workforces. This is a shift from the practice of treating agents as static service accounts or hardcoded API keys. The launch comes as federal agencies face mounting pressure from the recent executive order on AI innovation and security, which directs agencies to deploy AI agents and mandates that they secure them.

“The message to agencies is clear: Adopt AI aggressively, but secure it as you go,” writes Amy Johanek, Okta’s VP of Federal, in a blog post. “That puts identity at the center of the mission.”

“The fastest-growing class of NHI yet, and the hardest to see.”

Johanek also writes that AI agents are “the fastest-growing class of NHI [non-human identity] yet, and the hardest to see.” Anyone can spin one up, agents can spawn additional agents, and each connects across apps, APIs, SaaS tools, MCP servers, and data systems with little visibility, she says.

For organizations under mandates to harden systems and defend against AI-enabled criminal access, an unmanaged agent is not just an operational gap; it is more like an unguarded door, the company says.

“An unmanaged agent is not just an operational gap; it is more like an unguarded door.”

Johanek laid out four specific risks facing agencies running ungoverned agents: compliance violations when agents touch data outside authorized boundaries; compounding breach risk, where a single compromised credential doesn’t grant access to one system but to everything an agent can reach before a human can intervene; failed audits when agents run as orphaned accounts with no owner or evidence trail; and stalled AI adoption when delay becomes the only compliant option.

Moreover, the platform is organized around three governance questions: Where agents operate, what resources they can access, and what actions they’re authorized to take. Agents are registered in Okta’s Universal Directory inside an organization’s regulated cell, each assigned a unique identity and a named human owner, Johanek says. Every agent becomes a known, owned, first-class identity inside the environment, whether it came from a third-party platform or the organization’s own developers.

The platform replaces static credentials with scoped, short-lived tokens enforced at runtime. Least privilege is applied across authorization servers, third-party applications, and MCP servers. The governance layer mirrors existing federal workforce identity controls: access certifications, entitlement reviews, time-bound permissions, and a full audit logging stream that can be streamed to SIEM platforms for U.S. Government Accountability Office reporting requirements, Johanek says.

The offering also provides a kill switch

The offering also provides a kill switch. When an agent deviates from its intended mission or unexpectedly accesses sensitive data, security teams have a real-time mechanism to contain the risk before it escalates into a larger incident.

Johanek says she sees the offering as continuity rather than new infrastructure.  Agencies already trust Okta to manage human identities. Okta Identity Governance achieved FedRAMP High authorization earlier this year; bringing agents into that same identity fabric, she writes, is the natural next step, not a parallel system to build and defend.

However, there is one caveat: Okta for AI Agents – Core is not authorized in Okta for US Military cells.

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Amazon Web Services has added a third specialized “frontier agent” to its growing portfolio of AI tools aimed at IT operations — this one focused on the cloud bill.

AWS FinOps Agent, which the company moved into public preview last week, follows the earlier debuts of AWS’s Security Agent and DevOps Agent. It enters a domain that has historically relied on dashboards, spreadsheets, and a human analyst’s knowledge, and hands it to an agent that can be asked questions in plain English and that can act on its own when something looks wrong.

In this case, the domain is FinOps — the discipline of getting engineering, finance, and business teams to share accountability for cloud spending. AWS frames the new agent as a response to a shift it says is already underway: FinOps work moving from periodic, dashboard-driven reviews toward continuous workflows that run inside the tools engineering teams already use, namely Jira and Slack.

What the agent does

The core workflow starts where AWS Cost Anomaly Detection leaves off. Today, an anomaly alert tells a team that something changed; it doesn’t say what or why. FinOps Agent is built to take that next step — correlating the cost spike against AWS CloudTrail’s record of who changed what and when, identifying the triggering change, and assembling an investigation summary that names both a probable root cause and a responsible owner. From there, it can open a Jira ticket or post to a Slack channel automatically.

The agent answers natural-language cost questions, such as “Why did my AWS cost go up last month?” It does so by pulling from Cost Explorer, Cost Optimization Hub, and Compute Optimizer and tying the answer back to specific services and usage drivers. Organizations can upload context files mapping accounts to owners, teams, and tagging conventions, which the agent uses to translate a question like “what’s the cost of Team X” into the right set of accounts.

The public preview also adds scheduled cost reporting (daily, weekly, or monthly, exportable as HTML, PDF, or PPT) and a feature that bundles Cost Optimization Hub and Compute Optimizer recommendations into a Jira ticket engineers can act on.

The permission model is mostly read-only

For a tool that’s being given broad visibility into billing, usage, and operational data across an account, the access AWS is asking for is constrained. According to AWS’s documentation, the IAM role FinOps Agent uses is primarily read-only across billing, optimization, monitoring, logging, and infrastructure services — enough to analyze costs, investigate anomalies, and surface savings opportunities, but not enough to touch the resources themselves.

The only write access granted is for managing the agent’s own EventBridge scheduling rules, which drive its recurring automations. It can’t create, modify, or delete EC2 instances, RDS databases, Lambda functions, or networking components. The agent is built on Amazon Bedrock, which AWS says includes its standard automated abuse-detection guardrails.

Early customers

AWS’s announcement mentions four customer accounts, each describing a slightly different pain point the agent is meant to address. Workday‘s AI Platform Infrastructure team, which runs the company’s AI platform across many AWS accounts, described the appeal as consolidating two time sinks — “chasing down cost outliers before they become budget problems” and assembling the monthly reports leadership reviews — into one natural-language interface, according to Serjesh Sharma, Manager of Software Development Engineering at Workday.

Mitre 10, New Zealand’s largest home-improvement retailer, framed it in terms of competing priorities for a lean platform team. Eduard Kleynhans, the company’s Platform Engineering Manager, said recurring cost reviews and anomaly checks have historically “competed directly with reliability and improvement work,” and that the appeal of the agent is having those checks “run continuously in the background” so findings surface only “when there’s something that genuinely warrants attention.”

Convera, a commercial payments company operating in a regulated environment, pointed to a more specific failure mode: small, unintended cost changes that get lost in a shared queue. Ramesh Singaraj, the company’s Infrastructure Engineering and Operations Leader, said the agent’s value is that it routes a Jira ticket “to the engineering team that owns the resource, so the right engineer sees it instead of a shared queue that nobody watches.”

And AVIV Group, which operates digital real-estate marketplaces across France, Germany, and Belgium with hundreds of AWS accounts under a centralized FinOps team, framed the agent as a way to offload first-line questions, like the difference between on-demand and Savings Plan pricing, or why a particular anomaly fired, that currently route back to a small central team before resource owners can act. FinOps Director Jordi Espasa said answering those questions directly for engineers frees the central team to focus on “chargeback logic, optimization strategy and leadership reporting.”

What’s still unsettled

The preview is available only in the US East (N. Virginia) Region, though it can manage cost and usage data across other AWS Regions and accounts when deployed from a management account (GovCloud and the Beijing/Ningxia China Regions are excluded). It’s free to use during the preview, subject to a monthly usage limit, though standard charges still apply for any other AWS services the agent touches along the way.

AWS says the agent will expand over time, including cost analysis aimed specifically at AI workloads. This is notable given that AI infrastructure spend is becoming one of the larger line items FinOps teams are being asked to explain.

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