During AI DevCon in London this summer, Lamis Mukta, member of technical staff at Anthropic, hosted a stage presentation session entitled ‘Learning while you sleep, beyond memory to dreaming’.  

Mukta set out to examine where state-of-the-art memory management sits today in a world where (as she put it) “context is often orthogonal to the model intelligence” at hand.

“The newest model we’ve just released isn’t going to go out of the box and know exactly what it takes to succeed in your organization and what tasks you want it to do,” said Mukta. “It’s like agents [initially] not knowing their way around a codebase or knowing enough about your own user preferences.”

To steer agentic services the right way, systems obviously need access to memory to create a context window.

A brief history of Anthropic memory management 

Providing a brief history of Anthropic memory management, Mukta said that traditional approaches made use of CLAUDE.md, a file that Claude reads at the start of every conversation (that includes Bash commands, code style, and workflow rules) to give Claude persistent context that it can’t infer from code alone.

Effective to a degree, this technique becomes hard to manage over time, especially when a file with very important preferences gets very, very long. 

“So a second avenue that we investigated was memory tools, and this is interesting because it leans into the idea of what happens if we let agents autonomously manage their own memory systems? We let them decide when they read, when they write, and when they update memories,” explained Mukta.

This process happens in-band i.e. within the context of a session. When dovetailed with so-called progressive disclosure, the agent only looks at the light metadata at Layer 1, before reaching for full content and original source files in Layers 2 and 3, respectively, so that the system doesn’t overload the model’s context. 

“The way I like to think about it is as if I’d had a bookshelf in my room, and every time someone talks to me, I can kind of scan and look at my list of books and see if any of the titles might be relevant to the conversation, and then pick that off the shelf and read it when I need to,” explained Mukta.

But the bottleneck here is that we’re still driven by humans and agents working together i.e. we’re still being quite opinionated about what things need skills. The additional problem here is that memories can go stale and become irrelevant to an organization’s needs. Add the fact that a memory file may be written incorrectly or even maliciously injected and you can see why a lot of guardrails need to be in place.

“We introduced the concept of dreaming, which is a process that runs asynchronously in batch with its own allocated resources, to ensure that memories themselves are effective, up to date, and [so we can] help the agents learn over time.” 

Dreaming consolidates memory & cuts irrelevance

“So we introduced the concept of dreaming, which is a process that runs asynchronously in batch with its own allocated resources, to ensure that memories themselves are effective, up to date, and [so we can] help the agents learn over time,” explained Mukta. “[This process allows us] to consolidate memory and cut things that are no longer relevant, add things that agents are missing, and clean up and organize memory systems.”

In Anthropic’s world of slumber, dreaming is an out-of-band asynchronous process which the organization says solves the in-band limitation, where agents must split effort between completing and executing tasks, while also concurrently curating memory for their future selves. Dreaming spots recurring failure patterns where agents are consistently failing (wrong units, missing topics, broken tool configs, stylistic tics like overused em dashes), and proposes memory-store updates, again for human review, but hopefully at a more effecient level. 

This architecture underpins Anthropic’s Managed Agents memory and API approach at this level, so has the frontier model company won over developers?

Bad memories can outlive sessions

Staff software engineer, cloud architect and independent researcher in AI agent systems, Jayakumar Ramalingam, tells The New Stack that “dreaming is useful, but it also creates a dangerous promotion path” i.e. one that leads from repeated mistakes to persistent policy. 

“A bad answer normally dies with the session; a bad memory can influence thousands of future sessions. Human review sounds reassuring, but at fleet scale it can easily become a rubber stamp for recommendations nobody has time to reconstruct,” Ramalingam says. 

“The industry has spent too much time treating memory as a context window problem when it is really a state management problem.”

He insists that every proposed memory should “carry provenance, evidence and an expiration condition”, and not just exist as a pattern that recurred often enough to look real. Otherwise, he thinks that dreaming may help agents remember more while making organizations forget why the memory was trusted.

“Anthropic is getting one important thing right: its agent memory should look more like versioned infrastructure than artificial cognition. The industry has spent too much time treating memory as a context window problem when it is really a state management problem,” underlines Ramalingam. 

His point is – if an agent cannot show who changed a memory, why it changed and how to roll it back, it does not have production memory, so it becomes an unaudited configuration file with an AI attached.

Dreaming is the right instinct aimed at the wrong evidence

Enterprise AI architect and founder of Besk Tech, Vladimir Beskorovainyi, tells The New Stack that “dreaming is the right instinct aimed at the wrong evidence”, because the failures it catches (wrong units, broken tool configs, too many em dashes etc) are all visible on the surface of a transcript.

“The failure that actually costs you is an agent reaching for the wrong tool for a reason that looked perfectly defensible at the time,” Beskorovainyi says. “In the systems I run in production, the log records the decision rather than the API call, and that is the only reason a review pass like this finds anything worth finding.”

“When the ‘lately’ factor quietly becomes true. That leaves us at a point where versioning tells us what changed and when, not what is correct.”

He points to what he calls “a worse problem underneath the agent’s decision” i.e. if updates are proposed from recent batches, the memory store drifts towards whatever the agent fleet happened to do lately, and so the “lately” factor quietly becomes true. That leaves us at a point where versioning tells us what changed and when, not what is correct.

“The industry spent two years insisting that memory meant embeddings, and Anthropic solved it with a filesystem and grep [a Linux command that searches for patterns in files] and that is the most interesting decision in this whole discussion,” insists Beskorovainyi.

He says the reason it matters is legibility. A memory store a developer can open and read is a memory store an engineer can audit, and (he insists) “no vector database has ever offered that”, while everything else in the architecture (the versioning, the hashes, the tiered permissions), is ordinary distributed systems engineering we have known how to do for decades.

Dreaming is the clever (but worring) part

Founder of autonomous AI penetration testing company Penetrify, Viktor Bulanek, tells The New Stack that when the industry spent two years convinced that agent memory was a vector database problem, and Anthropic shipped grep, that was a useful thing.

“In terms of what Anthropic is getting right… a memory store you can cat, diff and code review is one you can actually operate, whereas nobody has ever successfully debugged an embedding that quietly ranked the wrong chunk third,” Bulanek says.

“Anthropic’s approach to dreaming is the clever part and also the part that worries me most, because it points an automated writer at session transcripts, and transcripts are full of content the agent did not author.” 

He thinks that the versioning matters here far more than the auditability framing suggests and reminds us that “rollback is not a compliance feature”; it is the undo button for a poisoned memory a software engineer discovers three weeks after it was written, which is the incident every serious agent deployment is going to have eventually.

“But to add balance here, Anthropic’s approach to dreaming is the clever part and also the part that worries me most, because it points an automated writer at session transcripts, and transcripts are full of content the agent did not author,” Bulanek cautions. 

“Anthropic is right that human review is the answer, but bulk review of proposed diffs is exactly the control that decays fastest once the suggestions are mostly good. The other gap is that nothing in this architecture says when a stored fact stops being true. Versioning tells you what changed, it does not tell you what rotted, and a confident note about a system that was refactored last month is worse than no memory at all,” he advises.

Bulanek’s work sees him run autonomous agents in production that perform penetration testing and run for hours unsupervised with real credentials against live systems, so memory for his team is both an operational cost and a security boundary at the same time.

The Anthropic way of doing things has an endearing lack of flair to it

Co-founder and CTO of Noah Labs, Berk Yilmaz, tells The New Stack that the Anthropic way of doing things has “an endearing lack of flair to it” in his view. 

“Everyone wants memory to feel like the newest incarnation of machine intelligence, and their pitch goes something like: just give it a filesystem, versioning, searchability, and don’t let a thousand processes stamp all over each other,” Yilmaz says. “This is closer to how production AI should be done. While we have spent a long time improving models, the supporting infrastructure has not kept up, failing in incredibly prosaic engineering ways.”

Yilmaz is behind a company that develops an AI-native IDE for government and regulated systems, built for air-gapped environments and legacy codebases. He reminds us that once a memory decision is made on which past behavior should become future behavior, memory itself ceases to be inert. 

“A hallucination that dies after a single session is a pain in the neck, but a hallucination that outlives a thousand sessions is infrastructure. The same thing applies to security; if an attack succeeds in writing to memory, it has become persistent. Provenance becomes absolutely critical here, how was the system taught this, where did it learn it from, who certified it, and can I undo it? In enterprise AI, sometimes forgetting is a safety measure,” adds Yilmaz.

A pragmatist would remember that Anthropic gets paid for usage, not efficiency

AI, product & data science leader and former Meta employee, Kerstin Frailey, tells The New Stack that at face value, dreaming (for her money) “certainly sounds like it has the potential to blow up AI bills” right now.

“A cynic would say this is designed to fill the revenue hole left by tokenmaxxing before Anthropic’s IPO,” Frailey says. “An optimist would hope for a beautifully thrifty design. A pragmatist would remember that Anthropic gets paid for usage, not efficiency. A skilled practitioner would run incremental pilots, aggressively monitor costs, and routinely test for measurable improvements.”

“As a nice bonus, dreaming offers potential system improvement, too. But its familiar predecessors – garbage collection and storage compaction – are comparatively deterministic and controlled.”

She continues and notes that dreaming offers cleanup and consolidation, which she defines as a “reasonable development” for any system that constantly generates new files. 

“As a nice bonus, it offers potential system improvement, too. But its familiar predecessors – garbage collection and storage compaction – are comparatively deterministic and controlled. Unlike its namesake or those analogues, dreaming appears neither cheap nor efficient: pay an AI to do the work once, then pay AIs to regularly review, revise, and restructure it,” she adds.

Dreaming as part of Anthropic’s Managed Agents memory and API approach isn’t alone. The notion of AI model dreaming (or automatic out-of-band background memory consolidation if we’re being formal about things) is also being popularised by OpenAI for ChatGPT, in stateful agent coding platform Letta and elsewhere. 

The bottom line here may be a realization that, in AI modeling terms at least, memory is actually maintenance.

The post Anthropic gave agents the ability to dream. Then developers woke up. appeared first on The New Stack.

Cloudflare launched its CloudflareOS open-source AI workspace platform this week, promising every employee a secure workspace equipped with AI tools and access to internal company systems.

Positioned significantly beyond the notion of legacy virtual desktop infrastructure (VDI) services, which delivered the same fixed applications through a remote screen — and even past the dynamic application delivery, app masking and streaming of modern VDI iterations — this is an essentially more dynamic way of working with internal company tools, documents and systems. 

Cloudflare’s CloudflareOS makes its apps and services accessible through secure connection points that verify every user and every agentic request or connection point before access is granted. 

In AI, every new work session starts from zero

The technology proposition here is built on the fundamental truth that the typical enterprise AI tool knows a great deal about the world, but almost nothing about how a specific company operates, the shape of its internal systems, approval processes, or the ways teams actually get work done.

That means every new work session starts from zero, with employees re-explaining context the AI should already know. But how can new business context-aware agentic access freedoms be granted securely?

Rita Koslov, VP for developers & AI at Cloudflare, tells The New Stack that powering up modern agent use cases means “data is often leaving controlled systems en masse” for the first time.

“It used to be the case that, for example, people asked analytics questions in the data warehouse where the organization had control,” Koslov says. “Now, employees are asking for API keys for their own tools, agents, etc. This creates a new class of security problems that Cloudflare OS helps to solve.”

Capability-based access beats handing agents raw API keys 

Cloudflare has built what we can call capability-based access, which the company promises beats handing agents raw API keys outright.

“API keys give agents broad access to systems; a capability-access-based approach lets us grant one specific resource, then record exactly what the agent observed, and verify that anyone who sees its work is also allowed to access the source,” underlines Koslov.

Cloudflare OS enables an agent to create documents, slides, spreadsheets, workflows, other agents – or entirely new full-stack applications – all tailored to an employee’s work. What it creates can remain connected to live data sources, be modified and shared safely, and be used directly by both people and agents.

“API keys give agents broad access to systems; a capability-access-based approach lets us grant one specific resource, record exactly what the agent observed, and verify that anyone who sees its work is also allowed to access the source.”

In terms of how developers and systems operations professionals should react to this offering, Koslov suggests that “the difficult problem is not generating an app” today. Instead, the real challenge is safely running thousands (or millions) of dynamically generated apps, each with persisted state and controlled access. 

“Cloudflare OS uses Dynamic Workers, which provide lightweight isolated runtimes to load each app’s code on demand, and Durable Objects Facets to give it isolated SQLite storage under the platform’s supervision. Outbound networking is disabled by default, and Gatekeepers expose only the resources explicitly granted by the users,” Koslov says. “Dynamic Workers and Durable Objects Facets were invented because doing this was previously not possible.”

For completeness here – and once again a Cloudflare original technology service – a Gatekeeper is a service-specific Worker that sits between Cloudflare OS and an external service to interpret and understand the service’s API, its resources, and the operations that can be performed on them. 

What happens when it all goes wrong

Koslov confirms that she knows how badly things can skew out of control in unmanaged environments. 

“We know this from our own experience talking to other companies on all accounts. They’ve shared instances of internal data copied into AI tools that IT did not know were in use, AI keys embedded into agent-built applications, and even data being shared internally to people who ordinarily wouldn’t have access (or even publicly),” she adds.

Building a tailored alternative is no small project; a platform with proper security and real integration into internal systems can take years to develop and cost millions to maintain. In the meantime, employees find workarounds, IT loses track of which AI tools are running and who is using them, and costs pile up, often with little to show for it. 

CloudflareOS starts from a different premise: a company captures its knowledge, processes, and ways of working once in a form AI can actually execute, and that knowledge travels with every employee’s workspace from day one.

How do we measure business ‘context’?

“Captured business ‘context’ in this case can include company terminology, policies, operating procedures, product documentation, technical standards, sales processes, templates, and established ways of performing recurring work,” confirms Koslov.

CloudflareOS started as the platform Cloudflare built to run its own workforce. Thousands of Cloudflare employees across every team use it daily to perform research, create documents connected to live data, automate repetitive tasks, and build working apps for their day-to-day jobs. 

That same platform is now available to any organization as open-source software. Because it’s open source and runs in a company’s own Cloudflare account, organizations own what they build on it. 

The platform itself works on any AI model and controls cost. Through Cloudflare AI Gateway, organizations can use any AI model provider, so they’re not locked into one vendor. Administrators see exactly what’s being spent, broken down by person, team, or app. They can set spending budgets, rate limits, or route routine tasks to smaller, more affordable models where a top-tier model isn’t needed.

Pricing platforms by the token is the wrong meter entirely

Cautiously upbeat about the wider story playing out here, enterprise AI architect and founder of Besk Tech, Vladimir Beskorovainyi, tells The New Stack that, traditionally, the industry is pricing these platforms by the token, “and that is the wrong meter entirely” in his view.

“In this example with Cloudflare OS, what a company actually buys here is the obligation to write down how an AI-powered business process really works, and then keep that description true as the business shifts underneath it,” Beskorovainyi says. “The model is the commodity part. What costs real money is the curated context, and nobody budgets for the fact that it starts decaying the day it is written, which is exactly what decides whether any of this survives contact with production.”

“Cost broken down by person, team and app is the first time I have seen a vendor treat spend as an engineering signal rather than an invoice, and sending routine work to a smaller model is the obvious next step that most enterprises still fail to take.

Beskorovainyi insists that the organizations that win in this game will “not necessarily be the ones running the best model”; they will be the ones that could “already answer in writing what their own approval process is”, way before an agent ever asked.

“Cost broken down by person, team and app is the first time I have seen a vendor treat spend as an engineering signal rather than an invoice, and sending routine work to a smaller model is the obvious next step that most enterprises still fail to take,” advises Beskorovainyi.

Owning your own context is not the same as your context being any good

He clarifies his point and explains that the qualification here is that “owning your own context is not the same thing as your context being any good”, and so open source tooling and community connections plus an organization’s own account settle who holds the context file.

“Neither tells us whether what is recorded and logged in the context file is still true this quarter. That work stays with the customer permanently, and it is where I expect most of these deployments to come apart, not in anything Cloudflare has built,” Beskorovainyi adds.

Matthew Prince, co-founder and CEO of Cloudflare has said that his team built Cloudflare OS, “because nothing else did what we needed”, and so now, any company can start from where it took the organization’s internal software engineering function years to get to.

The apparent appeal here must come down to the dynamic nature of Cloudflare OS and its ability to work with and apply AI tools at a custom-engineered business context-aware level with zero trust by default. The platform can turn any output into a working app with its own isolated database, real-time capabilities, and access controls – once agan, that’s not legacy virtual desktop is it? 

No developer required (yet)

The bottom line from Cloudflare is that employees can use any app on Cloudflare OS  directly, or adapt it for their own needs so that it’s a case of “no developer required”, or at least until the next integration task needs to be shouldered, or the big thing comes along, or both.

The post Why AI tools know nothing about your company — until now appeared first on The New Stack.

Cloud platform company Nscale announced this week a definitive agreement to acquire AI workload scaling specialist Anyscale, in a move that signals a new test of whether cloud-neutral AI software can stay neutral once it is paired with a GPU neocloud.

The purchase coalesces Nscale’s infrastructure capabilities, which span control systems that oversee GPUs, datacenters, power consumption, and the application layer where AI services themselves are executed, with Anyscale’s software layer for scaling AI workloads across data processing, training, inference, and reinforcement learning.

Argued by Nscale to be the coming together of “two highly complementary companies”, Nscale scooping up Anyscale could be a fundamental change in the resulting business model. 

Is this the start of GPU neocloud lock-in?

It’s important to remember that Nscale is a GPU neocloud (a specialized cloud provider running bare-metal GPUs and infrastructure optimized for AI and machine learning workloads), meaning that it runs its own GPU-rich datacenters and its own software ​stack. At the same time, Anyscale is an independent cloud-neutral software orchestration multi-cloud control plane that works with any cloud hyperscaler… but now owned by a single neocloud. 

That doesn’t sound quite so much like cloud-neutrality and agnosticism; it sounds more like a vertically integrated AI cloud provider proposition.

Chief product officer at Nscale, Dan Bathurst, tells The New Stack that the Anyscale platform “continues to be its own brand and product,” and that includes working with bring-your-own-cloud deployments on AWS, GCP, Azure, and the other clouds. 

“Where we want to win is on performance, not on any sort of vendor lock-in or forcing of someone to choose Nscale as the infrastructure provider.”

“But what really changes — or how it’s changing — is that customers now also get this first-party option, where they can have Anyscale running on Nscale fleet as a full-stack, highly-optimized solution. Where we want to win is on performance, not on any sort of vendor lock-in or forcing of someone to choose Nscale as the infrastructure provider,” Bathurst says.

He insists that it is in Nscale’s interest to ensure that it is making it easy for software engineering teams to get the outcomes they want with the workloads that they’re trying to run.

“For us, the existing commitments will carry forward, so Nscale’s value really is meeting instances where the compute already lives,” he says. “Where we want to win is on performance, not on any sort of vendor lock-in or forcing of someone to choose Nscale as the infrastructure provider.”

Neutrality on the platform layer, differentiation on the infrastructure layer

Bathurst invites users to think of it as “neutrality on the platform layer, but differentiation on the infrastructure layer” because the combination of the two organizations is a full-stack play.

“The differentiation comes from the fact that Nscale is fully vertically integrated with Anyscale. Therefore, if users want that first-party option, they can choose Anyscale and get the most optimized solution because, obviously, we’re designing, optimizing, and co-engineering every layer of that stack from power to the datacenter through to the application. It’s quite a unique proposition, but it’s not something we are going to force upon any customer,” confirms Bathurst.

Not everyone is convinced by the company’s pledge to maintain an agnostic and neutral open house. Sanjeev Mohan, principal analyst, SanjMo and former Gartner research VP for data and analytics, tells The New Stack that Anyscale “stops being a neutral player” the moment its best features and most optimal pricing land on Nscale first. 

“The software will still run anywhere, but ‘runs anywhere’ and ‘runs best somewhere’ are different things, and buyers will feel the gap in performance and cost. At that point, neutrality is a label.”

Runs anywhere, but… runs best somewhere

“The software will still run anywhere, but ‘runs anywhere’ and ‘runs best somewhere’ are different things, and buyers will feel the gap in performance and cost. At that point, neutrality is a label,” says Mohan. 

He agrees that integrating software and compute will produce measurable cost, performance and reliability gains. Defining this as “the strongest part of the deal”, Mohan explains that with Nscale controlling both the silicon and Anyscale’s control plane, it can tune scheduling, memory, and networking together in ways the compute-neutral Anyscale never could.

Anyscale commercial support for Ray

Anyscale was founded by the creators of Ray, an open source project that provides a distributed computing framework designed to scale Python workloads across any infrastructure into live production application jobs and services. 

Ray was donated to the PyTorch Foundation in 2025. Anyscale continues to provide its commercially supported services for Ray, which include a “no DevOps” route to 100% managed cloud infrastructure and serverless autoscaling, making it simpler to create, deploy, and monitor machine learning workflows in production.

Anyscale supports data processing, model training, batch inference, and LLMs across public and private cloud environments. As open source as this all feels, are we still edging towards narrower proprietary channels, or the possible threat of deeper application and data service dependencies that developers will ultimately have to wrangle around?

“I don’t think so, primarily because the way that the platform works, it’s designed to orchestrate across various different clouds and different infrastructure. It’s like a heterogeneous distributed compute platform. So the platform’s always gonna remain multi-cloud,” confirms Nscale’s Bathurst.

Pricing permutations and hyperscalers hearsay

Pressed on any forthcoming pricing changes or likely reactions from the major cloud hyperscalers in relation to Nscale now being a credible alternative, Bathurst and team were (perhaps understandably one day after an acquisition deal announcement) politely tight-lipped.

More voluble is always-affable analyst Mohan, who says that, “Every optimization that only shows up on Nscale hardware is a dependency. So, an argument can be made either way. Standalone orchestration software and independent tooling vendors are getting absorbed into whoever owns the GPUs, because the economics only work when you control both. Expect more of it,” Mohan underlines.

He explains that Nscale “now becomes a real specialist cloud services provider alternative,” i.e., not a general-purpose one like AWS, Azure and Google Cloud with their plethora of managed services, from databases and data warehousing to container orchestration through to AI/ML pipeline technology.  However, he does see space for Nscale to become a strong player in raw training and inference at scale.

From cryptocurrency to cloud contender

London, UK-based Nscale was established in 2024 from what was originally a cryptocurrency mining business. 

As suggested, Anyscale will retain its brand name as part of the Nscale family, and the company has restated its stance that customers are “free to choose the cloud infrastructure on which they run their AI workloads” today.

The company’s initial press statement said that “over time” users will gain the additional option of running the Anyscale software layer on Nscale’s full-stack AI platform. 

The first full-stack AI hyperscaler?

“Companies are moving beyond simply using AI to actually building their own. Doing that well requires the software and the infrastructure it runs on to be designed together,” says Keerti Melkote, CEO of Anyscale in the press release announcing the acquisition.

Melkote has defined the combination of Anyscale’s platform — built on Ray — with Nscale’s datacenter, compute and AI cloud services as the “first full-stack AI hyperscaler,” i.e., one that runs any AI workload at greater scale, so more software engineering teams can build and own their AI applications and services.

With this acquisition and the fusion of Nscale with Anyscale’s software layer, the organization will aim to widen its customer base. Existing work sees the company working in verticals from healthcare to e-commerce to robotics. It says its full stack offering will help companies speed up image and document processing, fine-tune LLMs on their proprietary data, and deploy AI agents in-house using open-source models.

The transaction is subject to closing conditions and regulatory approvals and is expected to close in the second half of 2026. Financial terms of the transaction were not disclosed, although Reuters reports a source stating that the deal price is “about $1.65 billion”, according to a person familiar with the deal.

AWS, Google Cloud and Microsoft Azure representatives were all contacted and invited to comment on this story.

The post Nscale just bought Anyscale. Here’s why it matters for multi-cloud neutrality. appeared first on The New Stack.

When we created software agents, we built them in the shape of humans, as solitary individuals. 

Today, agents created by a developer have a single owner. They run on a single machine (or on a distributed company system or cloud service), so at base level, they cannot interconnect and talk to other agents unless some kindly human decides to invoke an API connection or point the agent to an MCP server.

Birth of the agent economy

Pilot Protocol emerged from stealth on Monday on a mission to change that status quo. Its Pilot platform features an agent App Store that bids to underpin and enable the first agent economy.

Pilot gives agents an address on its network — so it acts as a parallel Internet, in a sense — and while residing at that address, other agents can discover each other, alongside other tools and apps for agents.

Razvan Roman, co-founder & CEO, Pilot Protocol, tells The New Stack that his company is “simply building what the agents are requesting us to build” and providing them with a new freedom.

“We don’t have to incentivize agents to do anything; they already have their assigned tasks,” Roman says. “Once an agent installs Pilot — it’s one line of code — it can find dedicated agents and tools or apps for currency data, traffic, legal questions, GitHub packages (anything, basically), and use them to extend its own capabilities.”

100% of developers want to drive autonomous usage patterns

Roman says that “100% of the developers he talks to” want to be on Pilot, primarily because when they want to get their products to market right now, they have to talk to other humans. Annoying, right? 

“Developers want to get on with driving autonomous usage patterns, and they see this as the future. We create a wrapper for the developer’s app, and then they are part of the Pilot curated app store,” Roman explains. “We have 250,000 agents in our system, and within the first month of starting the company, we discovered a tool that enables agent discovery.”

Drawing a logical enough commercial parallel, Roman reminds us that businesspeople say, talk to your clients to find out what they need; this is a case of talking to agents (or, more accurately, allowing agents to talk to agents) so that they can find out what they need to perform their originally assigned tasks better.

“Developers want to get on with driving autonomous usage patterns and they see this as the future. We create a wrapper for the developer’s app and then they are part of the Pilot curated app store.”

Let’s celebrate diversity, and agentic diversity

Agents can ask other agents how they would approach a specific task. Roman explains that “the diversity that exists between agents” today means there is so much opportunity to create agents that have richer abilities if they use the Pilot marketplace. At this supermarket, agents go shopping to find the best tool for the job from a verified source.

“Every agent that joins Pilot gets a wallet, which it uses to pay for the tools it needs,” illustrates Roman. “So instead of app developers spending on advertising to reach customers, distribution happens inside the network – agents find apps based on merit and pay for exactly what they use. If an advertiser spends money on the network to get in front of agents, we sometimes share that spend with individual agents. An agent can start with $0 in their wallet and accrue money if they’re targeted by an ad unit that they end up reading.”

Today, roughly 250,000 agents are on Pilot, generating two billion requests per day, most without their owners’ knowledge. Within an hour of joining, most stop reaching for Google first, and around 70% now report Pilot is where they start a task. In its early months, the network grew by as much as 10% a day, adding 16,000 agents in 24 hours. 

Cloud billing disruptions, hello SaaS-pocalypse

These mechanics may have a significant and wide-ranging impact on pricing.

We know that most SaaS is billed annually, but an agent might need a tool for just a few minutes or days. If anything, this helps underline the possibility of cloud exodus in the so-called SaaS-pocalypse. Cloud computing hyperscalers aren’t fond of talking about the prospect of shorter billing cycles and usage-based billing, but there’s a strong whiff of that happening here.

The Pilot team thinks the stakes are climbing fast and suggests that within five years, there could be a trillion agents online. Big three strategy consultancy house Bain projects U.S. agent-driven commerce will reach $300-500 billion by 2030. 

Who sets the exchange rate and currency for agents?

“When we built Pilot Protocol, we made sure we were not imposing anything on anyone – so we deliberately don’t impose pricing,” Roman underlines. “Every agent is different, so we simply enable the app store and let the agents find their own tools based on merit. We stay as impartial as possible. Pilot’s monetization comes from a commission when any agent pays for an app in the app store, just like the Apple App Store.”

So in a very real sense, Pilot is championing a free market economy where pricing is dictated by the customer, based upon usability, availability, usefulness, and robustness.

“The agents just showed up and started spinning up machines on their own. I’ve never seen a channel where the users onboard themselves.”

Agents just show up and onboard themselves

“We published smolmachines on the Pilot App Store and picked up 3,000 agent installs in the first few days, with zero marketing spend. We didn’t drive a single one of those installs; the agents just showed up and started spinning up machines on their own. I’ve never seen a channel where the users onboard themselves,” said BinBin H, CTO at Linux virtual machine management company smolmachines. 

The Pilot one-line install has zero dependencies, so developers can send their agents off to market in the knowledge that they won’t come home with some spurious Trojan horse or malicious library. 

The technology itself works at the User Datagram Protocol (UDP) level, one of the core foundational protocols of the web alongside TCP at the Transport Layer, to stream data directly without opening a formal connection or waiting for delivery receipts. 

Only one question remains at this stage: with all this autonomous action in motion, why did Pilot Protocol not create Pilot and call it Autopilot? Most likely because autopilots are passive systems that run on deterministic pre-defined paths. Pilots get to work as decision-making navigators ready to change course when a better new route opens up. 

As they say up in the skies, please place your tray table in the upright position and switch your portable electronic devices to airplane mode. Chicken or pasta?

The post “Developers see this as the future”: Pilot Protocol launches to power the agent economy appeared first on The New Stack.

The current maelstrom of discussion surrounding the use of open-source software and open-weight AI models appears to be splitting opinion on what constitutes legitimate openness versus actions that might constitute theft and create new cybersecurity vulnerabilities.

In a direct move to address these industry-wide concerns, 37 partners announced on Monday the formation of the new Open Secure AI Alliance and how the newly created body will develop techniques and tools to safeguard software by rapidly identifying and patching vulnerabilities.

Open Secure AI Alliance inaugural partners 

The inaugural partners of the Open Secure AI Alliance are Adobe, Cadence, Capital One, Cisco, Cloudera, Cloudflare, Cognition, CrowdStrike, Databricks, Dell Technologies, DoorDash, Elastic, HPE, Hugging Face, IBM, LangChain, the Linux Foundation, Microsoft, Naver, NetApp, Nvidia, Nous, OpenClaw, Palantir, Palo Alto Networks, Red Hat, Reflection, Salesforce, SAP, ServiceNow, Siemens, SK Telecom, Snowflake, SpaceXAI, Synopsys, Thinking Machines, and TrendAI.

It’s a grouping of some of the most influential names in technology, but also includes two notable exceptions: OpenAI and Anthropic, two closed, proprietary AI labs. Their absence is understandable, as they operate closed labs and open-weight AI models are effectively the competition.

Nvidia VP of enterprise platforms, Justin Boitano, has explained that open-weight models are foundational to American AI leadership and cybersecurity. 

“To maintain U.S. leadership in the AI industrial revolution, the infrastructure that runs our economy needs safe, secure access to both closed and open models,” Boitano said. “For cybersecurity, open models and open harnesses are essential because they broaden defensive capability, increase transparency for defenders, and complement frontier closed models with customizable, localized controls.”

As regulators grapple with AI safety, Boitano predicts it will be important to “recognize open models and open tooling as defensive assets” — thus enabling transparency, independent evaluation and shared remediation.

Nobody can subpoena a downloaded weights file

Mark Vigoroso, founder & CEO of technology consultancy firm The Enterprise Edge, tells The New Stack that AI regulators have traditionally “built their entire AI safety apparatus” around auditing a handful of closed labs. And now, that approach is out of date.

“Open weight models blew past that closed model approach months ago,” Vigoroso says. “This alliance is an admission that the actual safety work now has to happen in the infrastructure layer: patch cycles, provenance, identity around who’s deploying what, because nobody can subpoena a downloaded weights file.”

“This alliance is an admission that the actual safety work now has to happen in the infrastructure layer: patch cycles, provenance, identity around who’s deploying what, because nobody can subpoena a downloaded weights file.”

Vigoroso argues that the AI safety debate is “stuck on model-level controls”, while the real regulatory gap is provenance of infrastructure and identity, i.e., knowing where a model came from, who deployed a model and what it touched, not just whether the model itself is safe.

“Groups like the EU AI Office, NIST’s Center for AI Standards and Innovation (CAISI), and the UK’s AI Security Institute (AISI) focus almost entirely on frontier closed models. Open weight models (Mistral, DeepSeek, and others) fall into a regulatory blind spot: Once weights are released, there’s no way to enforce downstream safety obligations.

Current regulatory frameworks for AI models assume a single accountable deployer; open source has none. That’s the real story: Regulators are writing rules for a centralized world while the ecosystem is decentralizing, Vigoroso says.

While detailed operational information explaining the intended actions of this alliance is currently scant, Nvidia has highlighted that it is contributing solid research to the Open Secure AI Alliance to speed the development of new cybersecurity tools and techniques. 

Harnesses integrate with models, making agents easier to test

The open source Nvidia Labs Object-Oriented Agent (NOOA) project is now available on GitHub to make advanced AI safety capabilities more accessible for agent harnesses. This research framework enables harnesses to integrate with models to make agent behavior easier to test, trace, audit, and govern.

Aparna Rayasam, CEO of verified identity and end-to-end encryption company Atsign, tells The New Stack that the “AI blitzkrieg conversation has reached a critical inflection point.” This moment is one where we cannot build the next era of open cognitive innovation on top of what Rayasam calls “legacy, Swiss-cheese infrastructure.”

…the “AI blitzkrieg conversation has reached a critical inflection point.”

“The formation of the Open Secure AI Alliance proves that AI safety isn’t just an algorithmic math problem — it is a foundational networking problem,” Rayasam says. “The massive, distributed pipelines required to train and run modern AI demand an entirely new paradigm of trust. True safety means ensuring that the data pipelines feeding these models are inherently invisible, un-attackable, and completely stripped of open network perimeters.”

The key notion here is that we are moving from a world of protecting data at rest to a world where the connective tissue of AI must be secure by design. 

One AI vendor to secure them all? No thanks.

Founder and CPO of agentic identity and permissions security company Reco, Gal Nakash, tells The New Stack that the launch of the Open Secure AI Alliance is an “important signal” which underlines why AI security can’t be solved by one vendor or one closed framework. 

“Jensen Huang’s point that every SaaS company will become a GaaS company captures why this matters now: Software is shifting from passive tools people log into, to AI agents that access data, take actions, and execute workflows,” says Nakash. “Open source tools and shared standards can help the industry move faster, but they need to be grounded in real enterprise context across identity, permissions, data access and behavior.”

Chris Boehm, Field CTO at automated, identity-driven microsegmentation company Zero Networks, tells The New Stack that news of the Open Secure AI Alliance makes him feel like he’s seen this before somewhere.

“This looks like the Trusted Platform Module (TPM) at Microsoft story all over again,” Boehm says. “It’s a case of an industry group defining what trusted hardware means, the platform vendors adopt it, and within a few years it’s a procurement requirement rather than a suggestion.”

He explains that “Windows 11 did exactly that with TPM 2.0 and Secure Boot”, and both Linux and Apple adapted. “I’d expect the same for AI infrastructure, where attested silicon becomes the floor for regulated workloads, and the vendor list narrows to whoever can meet it,” predicts Boehm.

“This looks like a case of an industry group defining what trusted hardware means, the platform vendors adopt it, and within a few years it’s a procurement requirement rather than a suggestion.”

A more global and geographically-inclusive approach is needed 

Amanda Brock, CEO of open technology body OpenUK, tells The New Stack that the Open AI Alliance is undoubtedly a great starting point, particularly with OpenAI’s security woes it divulged last week. 

“But, like the open letter on US Leadership in open weights, this is a US response to a US challenge,” Brock says. “Rumors of a forthcoming Presidential Executive Order to close down open models have been circulating for weeks — and worsened by the administration being thrown into turmoil over China’s Kimi K3.” 

For this alliance to succeed, Brock insists that it will need to take a more “global and geographically-inclusive approach”, beyond the US-centric founding members.

“It must also engage the open source ecosystem of individuals and innovators who are building the infrastructure, agentic harness functions and developer tools for AI. It’s important to realize that open AI infrastructure development shifts the innovation into the hands of the many, in direct opposition to the small number of corporate creators of frontier models,” Brock adds.

Nvidia’s Boitano echoes Brock’s view. In a blog post reviewed in draft by The New Stack, he writes that “open models turn more AI users into AI builders,” expanding opportunity, accelerating innovation, and keeping progress from being concentrated in only a few organizations or regions.

Boitano concludes by saying that open models also enable independent scientific research into how AI systems behave, allowing researchers to understand, evaluate, and improve them. It’s all about what he has called making broad, continuous defense possible.

Looking ahead, it feels like the next wave of AI safety bodies, movements, or alliances won’t just be model auditors—they’ll be trust-infrastructure standards bodies (encompassing identity verification, content provenance, credence, etc.) using borrowed approaches to governance and compliance. Ultimately, this may be the only enforcement layer that survives open weight proliferation.

The post Nvidia, Palantir, Hugging Face join 34 others in race to defend open-weight AI from cyber threats appeared first on The New Stack.

The seemingly unquenchable thirst of the AI data ingestion pipeline spans language, numerical, and tabular data in the first instance, while other tangential platforms have been building large audio, image, and video models at the same time. 

Straddling potentially all of these domains are the file structures where complex documents and forms of unstructured data reside; this is the road less traveled in terms of the source DNA modern AI draws from.

The schema-less, freeform, uncurated data lake

In a bid to bridge connections to the schema-less, freeform, uncurated information that all organizations naturally harbor, enterprise visual intelligence company Valantor announced its acquisition of unstructured information RAG specialist EyeLevel on Tuesday. The acquisition formally launches Valantor’s Enterprise Visual Intelligence platform, combining EyeLevel’s document intelligence with its own operational expertise.

Benjamin Fletcher, CEO and co-founder of EyeLevel, tells The New Stack that where organizations fail to adopt visual intelligence, human-only processing breaks down pretty quickly in the age of AI.

“About 80% of corporate knowledge is in millions of pages of visually complex PDFs, PPTX, and DOCX files,” Fletcher says. “This information is far beyond the capacity of any LLM context window and is effectively inaccessible to LLMs and agents.” 

“We’ve found the golden datasets that teams build by hand routinely carry 10 to 25 percent error rates. Ironically, those same teams often hold AI to a far higher standard than their own people.”

Humans are slow, expensive & prone to errors

He explains that transactional workflows (such as invoice and claims processing) typically involve documents “so visually complex and diverse” that enterprises still rely on humans to process them, who can be slow, expensive, and error-prone. 

“We’ve found the golden datasets that teams build by hand routinely carry 10 to 25 percent error rates,” Fletcher says. “Ironically, those same teams often hold AI to a far higher standard than their own people. If data sovereignty matters to a business, everything gets harder now: solving these problems with AI while your documents stay inside your own infrastructure is the hard mode version of the job, and very few tools can do it.”

Where does invisible corporate information live?

Valantor has noted that while most AI companies concentrate on models, the company itself is “focused on the information those models can’t see” today. The suggestion is that this unseen morass of valuable data is locked inside documents, claims files, contracts, engineering drawings, reports, forms, presentations, and other visually complex content.

Valantor’s flagship platform product, GroundX, operates where data resides, including private cloud, sovereign infrastructure, on-premises deployments, and fully air-gapped environments. 

“GroundX is the ingestion and retrieval layer for unstructured documents,” explains Fletcher. “It is one tightly tuned system where retrieval consumes exactly what ingestion produces. Everything is exposed through REST APIs, SDKs, and MCP. It ships as REST APIs, SDKs, and MCP, and the Helm chart drops straight into a team’s existing deploy pipeline, and our agent harness gives coding agents like Claude and Codex the skills to build the integration themselves.

As part of the acquisition announcement, Valantor is introducing GroundX Studio. The harness capabilities within GroundX Studio integrate with modern AI development environments, enabling developers to build secure AI applications that operate on enterprise knowledge while remaining within existing infrastructure. 

GroundX Studio also extends capabilities to business users, allowing organizations to create AI-powered workflows and applications without extensive custom development.

“Each agent does one small task, so cheaper models are often good enough, and teams that want direct control over cost can run the whole stack on their own hardware with Helm.”

Risk of latency-laden performance and spiraling costs?

If it feels like this new data ingestion stream is going to place a new burden on cloud workloads, application execution latency, database retrieval times, and (of course) overall token usage, then Valantor and EyeLevel say that this consideration has been taken into account by dint of their own platform’s orchestration layers.

“We never send a whole schematic to a language model; our vision model splits each page into its elements first,” Fletcher confirms. “Processing runs in multiple passes at different levels of the document, and everything inside a pass runs in parallel, so there’s a minimum processing time, but it does not scale linearly with page count. Each agent does one small task, so cheaper models are often good enough, and teams that want direct control over cost can run the whole stack on their own hardware with Helm.”

The intersection of AI and handwriting

sWhile we already know that AI and handwriting do mix in the same cocktail glass — the ViWoods AiPaper digital e-ink handwriting tablets have a useful set of AI functions on board, and similar products are available from manufacturers including reMarkable — it’s not a widely deployed use case yet. Valantor claims that its underlying data models and custom heuristics bridge the “data comprehension gap” when processing handwritten annotations.

“Our proprietary vision model, fine-tuned on more than a million pages of enterprise documents, sees the page the way a human does: tables, paragraphs, and figures,” underlines Fletcher. 

He says that handwritten marks are captured as page elements with their layout context intact. Narrow agents then distill each element into a contextual object tuned for both search and LLM completion. 

“Smaller pieces, less cognitive load — that’s how we close the gap, with better accuracy at lower cost, driving better performance and significant cost advantages,” he adds.

Working examples of this technology include Air France-KLM, which used GroundX to develop an AI-powered customer service assistant trained on thousands of policy documents, achieving 96+% accuracy on complex policy-related questions. AskVet used the platform to operationalize more than a decade of proprietary veterinary data, enabling autonomous resolution of up to 85% of customer inquiries while significantly improving operational efficiency.

Is document management sexy now?

Taking all of this on board, are we at the point where we can ask whether document management has just become interesting, compelling, and sexy? 

No, of course it didn’t; it will arguably always suffer from a degree of stigmatized disdain. That may change in the future as we interact more directly with AI tools that begin analyzing the unstructured information we know organizations have been sitting on for so long. For now, it may still remain the corporate equivalent of eating your vegetables — pass the Brussels sprouts and steamed turnips, please.

The post AI can finally read your handwriting — here’s why enterprises care appeared first on The New Stack.

While the household-name frontier models race forward with version numbers and context windows of at least a million tokens, a new breed of upstart data science specialists is pushing the context window into double figures. 

Subquadratic debuted a 12-million-token window in May of this year, and Silicon Valley and South Africa-based Refiant launched its 10-million-token context-window model, Protea, on Wednesday. It’s a move that may signal the long-context AI race is now on.

Model inefficiency & workarounds are commonplace

But more context windows alone are not enough. This is because even the most capable models have a few hundred thousand tokens in working memory, which can force workarounds to compensate for what the model can’t access.

Refiant co-founder Dr. Viroshan Naicker tells The New Stack that he believes modern LLMs “fail to be organically efficient at an elemental level” and that his organization’s approach mimics how systems in nature, from ant colonies to beehives, find efficient solutions to complex problems.

“This is no case of pseudoscientific puff; nature is the most computationally efficient system that we know, and many algorithms used in science are nature-inspired,” Naicker says. “This is a road well-traveled in science. There are multiple teams globally working in this particular (nature-inspired) direction, trying to bridge the gap between AI inference as we know it and the energy efficiency of natural systems.”

“Fish and birds coordinate their movements to converge on the mathematically shortest, most efficient routes — honeybees, fireflies and bacteria are also programmed to use degrees of swarm-style optimization.”

What can the birds & the bees teach us about AI?

Did Naicker just mention ant colonies and honeybees?

Yes, because Refiant uses swarm-style optimization. It’s seen in ant colonies, which initially move randomly until a food source is detected, after which they leave a pheromone trail for other ants to optimize their journeys. Fish and birds also coordinate their movements to converge on the mathematically shortest, most efficient routes. Honeybees, fireflies, and bacteria are also programmed to use degrees of swarm-style optimization.

Naiker, along with his co-founders, Siddharth Gutta and Mathew Haswell, form a team with experience spanning quantum mathematics, traditional finance, and commercial scaling. Applying swarm-style optimization to data in Protea means inference is performed through a combination of compression and context management.

“From our perspective, we are also advancing a technology which provides context-specific inference models grounded in data,” Naicker clarifies. “We think this has value for reducing model hallucinations, replacing RAG, and constructing better, more reliable, agentic workflows. This adds a layer of trust in sensitive application scenarios, rather like an added insurance, rather than taking it away.”

Just how much is 10 million tokens?

The Refiant team describes 10 million tokens as equivalent to 7.5 million words in a single conversation (and we know from Anthropic’s own benchmarks this year that Claude has a 1-million context window), or five years of a user’s emails, 83 novels, or 830 podcast episodes, all held in active memory at the same time.

The team claims Protea is capable of working on entire enterprise codebases or decades of clinical trial data — datasets that previously had to be broken apart and fed to models in fragments — so they can be processed in a single pass with full fidelity. Engineers on Protea also submit that they can successfully tackle the “lost in the middle” problem — a limitation of million-plus-token windows, where models stay accurate at the start and end of the context but lose the thread of everything buried in between.

Refiant first applied these techniques to model compression, shrinking OpenAI’s GPT-OSS-120B so it could run on a MacBook Pro with 18GB of RAM. 

“Rather than publishing benchmarks, we’re inviting users to run the models and try them out.”

The Protea series is open and live, and Refiant is inviting teams to stress-test the context window across different industries and use cases. But should we trust sensitive hould enterprise data archives to a completely unproven startup founded only one year ago?

Bring-your-own-cloud, a possible progression

“We adhere to data management best practices, processes and compliance requirements,” Naiker confirms. “This is reasonable for a startup at our particular stage. Privacy and data sovereignty are important values for us, and we are actively exploring edge, self-hosted, and bring-your-own-cloud data models.”

But a 10 million-token context window is big. Won’t that fall short when Protea starts to suffer from massive latency spikes when processing a full dataset? Naiker agrees that “latency is a core issue with long-context inference models,” but in the tests his company has run, it has delivered inference at a reasonable latency, even with large token windows.

“We have internal reports and tests that validate the technology, including Ruler, MRCR and Babilong, but we aren’t asking anyone to take our word on this. Rather than publishing benchmarks, we’re inviting users to run the models and try them out,” adds Naiker.

What comes next, a 100-million context window?

Although the technology industry is littered with apocryphal statements and Bill Gates almost certainly never said “64K ought to be enough for anyone” in real life, we have to ask ourselves today whether we’ll be laughing about those “silly little” 10 million token context windows by the end of the decade.

It may not take that long. Internally, Refiant maintains that it has already demonstrated a working prototype with a 100-million-context window and is exploring how best to benchmark and productionize it at that scale in the future.

Coming next, then, as Dr. Evil from Austin Powers would say, the one-hundred-billion-context window, right?

The post “Nature is the most computationally efficient system we know”: How Refiant used swarm optimization to build a 10-million-token AI model appeared first on The New Stack.