Venture Beat

DeepSeek’s recent decision to drastically cut pricing on its V4-Pro model by 75% should have been unequivocally good news for enterprise AI vendors and developers. Instead, many are discovering that cheaper models don’t automatically translate into healthier margins.The reason is simple: While inference costs plummet, agent systems are voraciously consuming tokens faster than prices are declining. For the last 2 decades, software economics was dictated by the same rule. Infra became cheaper every year whereas applications became more capable. AI was initially hypothesized to follow the same pattern. As frontier models improved and token prices dropped, many assumed inference would become a negligible operating expense.That assumption has begun crumbling exponentially. A chatbot usually turns one user question into one model call. An agent turns it into a chain of planning, retrieval, tool use, verification, summarization, and follow-up decisions. The user sees one answer. The vendor pays for the loop. That is the 100x problem: The same user-visible request can cost a lot  more to serve as an agentic workflow than as a chatbot or retrieval-augmented generation (RAG) response. In longer-running workflows, the multiplier is higher. Falling model prices help, but they do not fix a product architecture that turns one prompt into dozens of billable operations.The scale of what is now at stake is clear in how model providers themselves are pricing developer relationships. OpenAI’s proposed program to give every Y Combinator startup $2 million in API credits — a number that would have funded an entire seed round in any prior tech cycle, and when the same cohort got by on a few thousand dollars of AWS credits — is less a recruiting perk than an admission of what it now costs to run an AI-native company through its first year of product. For established enterprises retrofitting agents into existing product lines, the absolute numbers are larger still.What token amplification isIn a single-turn chatbot, one user message produces roughly one model call. Input-to-billed ratio is about 1:5.In a multi-step agent rolled out across customer support, sales operations, finance, legal review, and engineering, that ratio routinely lands at 1:700 or higher. Every loop iteration carries forward the cumulative conversation, tool outputs, and reasoning traces. Each step appends; nothing is dropped.A “simple” agent query like “What did our top customer ask about last week?” typically touches seven priced operations before returning an answer:User prompt (~50 tokens)System prompt and tool definitions (~3,000 tokens, repeated on every call)Retrieval (~5,000 tokens of context)Model call #1 — tool selection (8,000 in / 200 out)Tool execution (~4,000 tokens returned)Model call #2 — summarization (12,000 in / 400 out)Model call #3 — follow-up decision (12,400 in / 100 out)One sentence in, roughly 35,000 input tokens billed. Somewhere between $0.10 and $0.40 per query on a frontier model. Multiply that by a million queries a month — the table-stakes volume for any enterprise B2B feature — and the line item is six figures.Why this breaks the existing AI business modelThe dominant pricing story for enterprise AI has been seat-based SaaS: Pay per-user per-month, deliver agent capability, capture margin. That model assumes a reasonably bounded cost-per-user.Token amplification breaks the assumption. A power user running 50 agent invocations a day on a $40/seat plan can cost more in inference than the plan charges. Token amplification shatters the traditional SaaS pricing model. When a power user’s daily agent activity costs more in inference than their monthly subscription fee, vendor gross margins turn negative, a paradox that compounds as customers deepen their agent adoption, the very usage curve vendors are selling to their boards. Several vendors are now privately reporting negative gross margins on heavy users, mirroring recent cloud expenditure reports from the Bessemer ‘Supernova’ cohort, where the correlation between AI-agent adoption and gross margin contraction has moved from a theoretical risk to a primary P&L headwind.The visible symptoms have started leaking into public coverage. Bloomberg this week documented a widening gap between Salesforce’s Agentforce marketing demos and the capabilities actually shipping to customers. This is the kind of gap that opens predictably when promised functionality is technically possible but uneconomical to serve at the price the seat plan implies. Salesforce is the most-watched case, not a unique one.”For my team, the cost of compute is far beyond the costs of the employees.” — Bryan Catanzaro, VP of Applied Deep Learning, NvidiaThe strategic implication is not “AI is expensive.” It is that the dominant business model assumed by most AI-native company plans does not survive contact with agentic workloads. A simple exampleConsider an enterprise software vendor charging $40 per-user per-month for an AI-enabled support assistant. A traditional chatbot might cost only a few cents per user per day in inference, leaving healthy gross margins.Now replace that chatbot with a fully agentic workflow capable of investigating tickets, querying internal systems, drafting responses, validating outputs, and escalating exceptions. If a heavy user executes 50 to 100 agent requests per day, inference consumption can increase by an order of magnitude. What was once a negligible infrastructure cost becomes a material operating expense.This creates an unusual dynamic: The customers receiving the most value from the product are often the customers generating the highest inference costs. In extreme cases, vendors can find themselves with their most engaged users contributing the least profit. The result is a growing realization across enterprise software that agent adoption and margin expansion are no longer automatically aligned.Agent orchestration is the new moatThe technical responses are known and converging. They are not novel, but they are critical for survivalCost-aware routing: This technique involves a small classifier model that decides which tier (Haiku, Sonnet, Opus equivalents) handles each query. Well-tuned routers cut inference bills by around 60% without any degradation in qualityPrompt caching: Anthropic, OpenAI, and Google now offer 75 to 90% discounts on cached prefixes. Context discipline: You can truncate tool outputs, prune reasoning traces, and cap tool depth to prevent your agent from going down a rabbit holeSpeculative decoding: for self-hosted deployments, this technique guarantees 2 to 3X effective throughput on the same GPUs.”Organizations using orchestration-led governance report stronger productivity gains — a holistic orchestration layer is associated with six times greater productivity impact than compliance‑only approaches” — IBMThe companies building this layer well are starting to look less like microservice operators and more like financial trading systems: Every routing decision priced, every path with its own P&L, every tenant on a metered budget.What enterprise leaders should actually doFour moves separate the companies that will still have margin in 24 months from the ones that won’t:Make inference cost a first-class metric. Track it per-feature, per-tenant, per-query class the same way cloud cost was tracked starting in the mid-2010s.Budget like a media buyer. Set cost-per-thousand-queries ceilings per feature. Cap them. Alert on overruns. Engineering will not enforce this on its own.Treat the router as core infrastructure, not an optimization. It is the new load balancer.Audit prompts quarterly. A 4,000-token system prompt that grew organically over six months is a six-figure bill in slow motion. Most teams have never read their own production prompts end to end.Negotiate volume commits early. Frontier-model vendors now offer reserved-instance-style prepaid commits at substantial discounts. List price is the worst price any enterprise will ever pay.The next 24 monthsThe structural shift underneath agentic AI is not that it is expensive. As DeepSeek’s price cut today underscores, frontier inference unit costs are dropping roughly 3X per year, and the curve is not slowing.The shift is that amplification is outrunning the price cuts. Cutting per-token costs 75% does not help a company whose agents are doing 700X more tokens per user query than its pricing model assumed. For the first time since the cloud era began, architecture decisions are again financial decisions in real time. A prompt redesign is a margin event. A poorly bound agent loop is an outage with a credit card attached.The companies that survive the next 24 months of AI infrastructure pricing will not be the ones running the cheapest model. They will be the ones whose agents are smart and know what they cost to think.That is the 100X problem. And it is arriving faster than the price cuts can hide it.Maitreyi Chatterjee is a senior software engineer at a big tech company.Devansh Agarwal works as an ML engineer at a leading tech company.

Slopsquatting represents an emerging supply chain threat made possible by AI hallucinations. As developers increasingly rely on AI coding assistants, they unknowingly grant cybercriminals access to their software from day one. Understanding what slopsquatting isSlopsquatting is a new type of supply chain attack that uses large language model (LLM) hallucinations to inject malicious code into development workflows. The term combines “AI slop” and “typosquatting,” a deceptive practice where attackers register misspelled or lookalike versions of popular domains to prey on users who enter URLs incorrectly.This novel attack vector exploits LLMs’ tendency to generate fictitious software package names, which threat actors can then register and populate with malicious code.During AI-assisted coding, the model may generate fake open-source packages — bundled collections of files, programs and installation tools. This alone is not necessarily harmful. However, if an attacker registers that fake package name, they can inject malware that gets incorporated directly into a developer’s codebase.How AI creates a supply chain riskTraditionally, AI safety risks stem from hallucinations, which can adversely affect users who treat misinformation as valid. However, those same hallucinations have evolved into exploitable security vulnerabilities.Typosquatting is a deceptive practice where a cybercriminal registers a mispelled version of a popular package to trick developers. It has existed for decades, so registries have built protections against it. However, AI has changed the threat model. It recommends fictitious packages that sound plausible rather than making simple misspellings. Once attackers learn which hallucinated packages models tend to invent, they can register malware-filled packages under those names.Since the hallucinated packages are not simply typoed versions of popular libraries, there are no protections against this practice at scale. For example, the registry protects against an attacker publishing “crossenv,” a squat of the popular “cross-env” package. However, it would not identify “mpn install cross-env file” or “cross-env-extended” as threats.Hallucinations are persistent and severeEven if many LLMs recommend the same hallucinated package, widespread compromise is still possible. Malicious packages could remain undetected in production for months or even years, allowing threat actors to passively inject malware across countless environments. One research team analyzed 31,267 vulnerabilities belonging to 14,675 packages across 10 programming languages. They discovered that reported vulnerabilities are increasing at an annual rate of 98%, faster growth than the 25% annual increase in the number of open-source software packages. The team also observed an 85% increase in the average lifespan of vulnerabilities, indicating a decline in security.Real-world dangers of AI hallucinationsMalicious actors can create open-access packages under the same name as commonly hallucinated libraries. Instead of standard code, they are filled with malware. The models believe they are referring to existing packages, so they often repeat the same hallucinated names. Since the hallucinations are not random, attackers could theoretically register packages that trick tens of thousands of developers.These packages appear legitimate. String similarity to real libraries makes them recognizable. One-character typos suggest simple mistakes rather than malicious intent. Even fully fabricated names remain believable when the AI presents them in proper context. Detection is challenging, as developers trust their coding assistants to recommend valid dependencies.Why are LLMs hallucinating packages?LLMs generate the statistically most likely answer rather than prioritizing accuracy. Hallucinations are relatively common as a result. One study found hallucination rates range from 50% to 82%, depending on the model and prompting method. Even GPT-4o, the best-performing model, goes no lower than 23%, even with prompt-based mitigation.Adversarial hallucination attacks could worsen this problem. Threat actors can leverage token-level manipulation or retrieval poisoning to force models to hallucinate in ways they want, increasing the likelihood that models recommend their malicious packages.Which LLMs are prone to slopsquatting?While all LLMs are prone to slopsquatting, some are more vulnerable than others. The likelihood of producing hallucinated packages during code generation depends on the model. Proprietary models are four times less likely to generate hallucinated packages than open-source models.One research group proved this by conducting 30 tests across 30 different systems. Out of the 576,000 code samples and 2.23 million packages it produced, 19.7% were hallucinations. GPT-4.0 Turbo had a hallucination rate of 3.59%, while DeepSeek 1B, the best-performing open-source model, reached 13.63%.This research suggests that organizations relying on open-source AI tools for code generation are roughly four times more exposed to slopsquatting attacks. That doesn’t necessarily mean proprietary tools will always remain safer, though. Once attackers realize this disparity, they may manipulate proprietary LLMs to take advantage of perceived safety.Vibe coding contributes to the problemSoftware developers who use AI tools estimate that over 40 percent of the code they commit includes AI assistance. They expect that percentage will increase considerably within the next few years. Already, 72% of those who have tried AI use it daily.The uptick in vibe coding and AI-assisted coding amplifies the threat surface. As more developers integrate AI tools into their workflows without implementing proper verification processes, the attack surface for slopsquatting continues to expand.For those using AI to assist with coding, double-checking output is essential. Verifying that recommended packages actually exist in official repositories before incorporating them into projects reduces risk.Navigating AI-assisted developmentImplementing automated checks that validate package names against known registries can help catch hallucinated packages before they enter production code. Security teams should also monitor for unusual package installations and maintain up-to-date threat intelligence on known slopsquatting campaigns.Zac Amos is the Features Editor at ReHack.

An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pulled. The model did not fail. The context it was given did.In the past six months, 57% of enterprises traced a confident but wrong AI agent answer to missing or inconsistent business context, and 31% said it happened more than once, according to a VB Pulse June 2026 survey of 101 qualified enterprises with more than 100 employees.The reason is not hard to find. Retrieval over documents is the default way agents get business context for 38% of enterprises, nearly double the next closest approach. The way most enterprises choose a retrieval system compounds the problem. Ease of ingestion and operational simplicity lead the selection criteria, with retrieval accuracy running behind both. The accuracy problem only shows up after the system is already live.There is a known fix for this, a governed context layer every agent reads from instead of guessing. Vendors are racing to roll out context platforms while most enterprises are still figuring out what it is.75% don’t have an agentic context layer yetThe context layer is meant to be a shared model of what business data actually means, built once and referenced consistently instead of re-derived by every agent that touches it. The VentureBeat research shows the enterprise response to that idea is broad but unfinished. Twenty-five percent of respondents run one in production. Thirty-four percent are building one right now. The remaining 41% have not started.Among companies already building or running a governed context layer, 78% report a confident-wrong failure — an AI agent that answered with total certainty and was still wrong. Among companies with no plans to build a layer, only 20% report the same thing. Companies that already got burned are far more likely to be building the fix. Companies that haven’t been burned yet see no urgency.What governed context looks like when someone actually builds oneEvery major data and AI platform vendor is now building some version of this layer, and they are not converging on the same architecture. DataHub is treating catalog metadata and years of analyst query behavior as a knowledge source, then keeping it current as a living system rather than a static wiki. Microsoft’s Fabric IQ is building a business ontology that any agent, not just Microsoft’s own, can query over MCP. Couchbase is pushing agent memory and context retrieval down to the edge, arguing the operational database is a more natural home for it than a search or analytics layer bolted on after the fact. Pinecone’s Nexus is compiling structural logic into the metadata layer ahead of runtime, betting that agents need pre-built structure more than they need faster search.Snowflake runs a two-layer system, Horizon Context for customer-managed definitions and Cortex Sense for context the platform infers on its own. Oracle’s Unified Memory Core takes the opposite approach, folding vector, graph and relational data into one transactional engine so there is no sync layer left to go stale. Google’s Knowledge Catalog mines query logs and usage patterns to curate semantic context automatically.AWS’s Context service makes the same bet, a knowledge graph that gets smarter from how agents actually use it rather than from manual re-curation.Analysts converge on one diagnosisThe vendor approaches differ. What analysts and practitioners have told VentureBeat about the underlying problem, across a run of interviews this year, does not.When DataHub’s context layer push landed this spring, Constellation Research VP and principal analyst Michael Ni framed the stakes in blunt terms. “Whoever controls runtime context controls the AI decision layer for enterprise data,” Ni said. He was equally direct about how far any single product actually gets a buyer. “Vector memory isn’t business meaning, business meaning isn’t governance and governance isn’t execution,” Ni said.In the same interview, BARC analyst Kevin Petrie pointed to a narrower but concrete gap. Most context platforms concentrate on structured tables, he said, which give agents trusted facts but miss the harder, messier context locked in documents and unstructured content, exactly the material a business actually runs on day to day.Stephanie Walter, practice leader for AI Stack at HyperFRAME Research, made a related point earlier this year when VentureBeat asked her about enterprise context fragmentation. “The market is converging on the same conclusion,” Walter said. “Agents don’t just need more tokens or better models. They need governed, current, low-latency context.” She made a similar case in an earlier review of Pinecone’s Nexus launch, careful not to overstate how new any of this is. Nexus, she said, “shifts knowledge work from runtime chaos to pre-compiled structure. But it’s an evolution of RAG architecture, not a complete reinvention.” Gartner’s Arun Chandrasekaran, reviewing the same launch, offered the more forward-looking read. Agentic AI, he said, is moving from pure information retrieval toward a reasoning architecture, one where long context works as short-term memory and a vector database functions as deep storage underneath it.The fragmentation problem shows up hardest at the practitioner level, where separate tools for retrieval, memory and access control were never built to agree with each other. Steven Dickens, CEO and principal analyst at HyperFRAME Research, put it bluntly after Oracle’s AI database push landed this spring. “Data teams are exhausted by fragmentation fatigue,” Dickens said. “Managing a separate vector store, graph database and relational system just to power one agent is a DevOps nightmare.” Matt Kimball at Moor Insights and Strategy, in that same story, put the production reality more simply. Getting an agent working is not the hard part, he said. The struggle is running it in production, where the goal becomes removing the distance between data and execution rather than adding another layer on top of it.What this means for enterprisesHere’s what this adds up to for enterprises building on this layer.Retrieval alone will not close the context gap. RAG is the default source for context in most enterprises today, and it is also the layer most closely associated with the confident-wrong-answer failure. Adding more documents or a bigger index does not fix a definition that is inconsistent across systems.The semantic context layer is where the budget is actually moving, even where it hasn’t shipped. Fifty-eight percent of enterprises are already engaged — building or in production — but only 25% have actually gotten a layer live. That gap shows where enterprises have decided to spend, not where they’ve arrived.No single vendor owns the architecture yet, and that is likely to stay true for a while. Enterprises evaluating this layer should expect to integrate rather than pick a single winner, at least for the next several quarters.The buying decision is happening this year, and it is concentrated among the companies already burned by it. Fifty-seven percent of enterprises plan to switch or add a retrieval or context platform within the next twelve months. That intent is not spread evenly. Enterprises that reported a repeat confident-wrong failure plan to switch or add a provider at roughly 81%, against 32% among enterprises that never hit the problem. The companies shopping for new context tooling right now are largely the ones whose agents already got it wrong. The agents are already running. The context underneath most of them is still being built, and the vendor selling the fix is being chosen this year.This data will be part of a broader conversation at VB Transform 2026 on July 14 and 15 in Menlo Park: the context gap enterprises are racing to close, and which of the emerging approaches — governed semantic layers, hybrid retrieval, provider-native bundles — actually holds up in production.

OpenAI on Thursday launched ChatGPT Work, a new AI agent embedded inside its flagship chatbot that aims to transform ChatGPT from a question-and-answer tool into an autonomous work platform capable of executing complex, multi-step tasks across users’ email, calendars, code repositories, and messaging apps.The product is powered by OpenAI’s latest flagship model, GPT-5.6, and is designed to go far beyond generating text. ChatGPT Work can gather context from connected apps, files, and workflows to produce finished documents, spreadsheets, presentations, reports, and websites. The agent takes a stated outcome, breaks it into smaller steps, and stays with complex projects for hours, completing them independently.The launch marks OpenAI’s clearest attempt yet to reposition ChatGPT as a workplace platform rather than a chatbot — and it arrives at a moment of extraordinary financial significance for the company. Last month, OpenAI confidentially submitted a draft S-1 registration statement to the SEC, initiating what could become one of the largest technology IPOs in history, with reported valuations clustering between $730 billion and $852 billion and annualized revenue that has blown past $25 billion.In a short demonstration and conversation with VentureBeat on Friday, Ty Geri, a product manager at OpenAI who helped build ChatGPT Work, said the product’s mission is to democratize the kind of agentic AI capabilities that OpenAI’s internal engineering tool, Codex, has already demonstrated. “What’s really exciting is we’ve seen how much Codex has been able to push the frontier of what we can get done with these AI tools, as opposed to just getting information or answers or guidance,” Geri said. “Our internal adoption of Codex is literally an exponential curve across every single product function and every single use case.”Why OpenAI built a persistent virtual machine that works from the beachThe core architectural bet behind ChatGPT Work is a persistent cloud-based virtual machine that runs on OpenAI’s servers, always available to the user regardless of which device they happen to be on. That marks a deliberate departure from competitors whose agents require a local machine to remain powered on and connected.”What’s really exciting about ChatGPT Work is that it’s a virtual machine in the cloud that’s always on for you, and this is available across all of our paid tiers,” Geri said. “All Plus users are getting this. I think that’s a very unique aspect of this.”The mobile-first aspect of the launch is something Geri described as “missing from the market.” He pointed to the ability to create a website on a phone and share it with collaborators as a particularly novel capability. “Sites are new in general to Codex. They launched in Codex about a week and a half ago, but now we’re launching also in web and mobile. You can create a site on your phone at the beach and share it with your friends,” he said.ChatGPT Work will roll out beginning with Pro, Enterprise, and Edu users, and will expand to Plus and Business users over the next few days. In the interview, Geri emphasized that the availability of the product to Plus subscribers — not just premium tiers — is central to OpenAI’s strategy. “It’s accessible to all paid plans, including Plus users, which in my opinion is a really big feat, and really part of that OpenAI mission, which is about bringing all this power to as many people,” he said.How MCP plugins connect ChatGPT Work to Slack, Gmail, and GitHubThe product relies on MCP-based plugins to connect to external services like Gmail, Google Calendar, Slack, and GitHub. When asked whether the plugin architecture is based on the Model Context Protocol standard, Geri confirmed: “These are all based on MCP.” He added that connecting multiple Gmail accounts — a frequent user request — “is definitely on the roadmap.”The experience is designed to be action-oriented from the first interaction. ChatGPT Work offers a personalized onboarding flow that surfaces different suggested use cases depending on the user’s role. Geri demonstrated how the system, detecting his role as a product manager, immediately suggested tasks like evaluating AI systems, building research artifacts, and managing his calendar. “You can start with a simple task like catch me up on Slack or Teams or read today’s calendar,” Geri said. He described a scenario where the system reviewed his calendar, identified scheduling conflicts, flagged meetings requiring preparation, and then — on his instruction — declined, accepted, or rescheduled events directly.Users can also customize the agent by teaching it their writing style, organizing outputs into projects, and — in a lighter touch — choosing a virtual pet that accompanies them in the interface. The interface also introduces a hosted website feature that allows users to build and share interactive sites directly through ChatGPT Work, turning what would typically be a static slide deck into a dynamic, collaborative artifact. “Now we suddenly have a collaborative interface that’s actually more exciting and more accessible than a slide deck, which has all these formatting restrictions,” Geri said.Scheduling 10 bug bashes at once: what agentic productivity looks like in practiceGeri’s own usage of ChatGPT Work illustrates the breadth of tasks the system can handle. In the run-up to the product’s launch, he needed to organize pre-release testing sessions — known internally as “bug bashes” — across dozens of features and team members.”I just come to ChatGPT Work and say, ‘Set up a bug bash for all the distinct features in ChatGPT Work. Add all the people that worked on that feature,’ and it can check Slack, it can check GitHub, it can check Docs, and find a time that works for the four highest contributors to that feature,” Geri said. “It went and scheduled 10 bug bashes, all coordinated across all those different people. That would have taken me 30 minutes at least.”But Geri pushed back against the characterization that ChatGPT Work is limited to rote administrative work. He described using it for analytically complex tasks like identifying the biggest causes of user churn for specific product features and generating product solutions — work he said would previously have taken months. “Things that we would have spent three months doing, we can now spend a week doing — and do much more, and make a much better product,” Geri said. “Bugs that we would have found three or four weeks from now, we can now find within two days and fix for our users.”He also described handing off the tedium of product testing itself. “It used to be that even though like the most interesting part of my job is like what to test, I would actually end up having to spend most of my job doing the testing, which is like me taking a mouse and like clicking on the same thing over and over again, like five times,” Geri said. “Instead, now I can define what do we want to test, and ChatGPT Work or Codex can actually go test it for me, deliver me that bug report, and then we can work on fixing that bug.”What OpenAI says about data privacy when AI reads your Slack and emailWhen pressed on data privacy concerns — given that ChatGPT Work pulls sensitive information from workplace tools like Slack, Google Drive, and email — Geri said privacy “is incredibly important, and the most important part of this is it’s always in the user’s control.”He pointed to OpenAI’s existing enterprise security infrastructure, noting that “enterprise accounts have ZDR, and users can always opt out of letting their conversations help improve future models, which many users do.” The comment aligns with assurances OpenAI made when it first launched ChatGPT Enterprise in August 2023, when the company wrote in a blog post that it does “not train on your business data or conversations.”The privacy question carries additional weight now because of the sheer volume of sensitive workplace data ChatGPT Work is designed to access. Unlike a chatbot session where a user voluntarily pastes text into a prompt, ChatGPT Work actively reaches into connected systems — reading Slack messages, scanning calendar invitations, pulling GitHub commit histories — to assemble context for its tasks. That represents a fundamentally different data surface area than anything OpenAI has offered before, and one that enterprise security teams will scrutinize carefully before granting access.ChatGPT Work enters a three-way arms race with Anthropic and MicrosoftChatGPT Work lands squarely in the middle of what has become the defining competitive battlefield in enterprise AI: the race to build autonomous workplace agents that can go beyond generating text and actually execute tasks.The product arrives months after Anthropic took Claude Cowork out of preview and into general availability in April, bringing its AI agent to web and mobile platforms aimed at helping enterprise users monitor and manage long-running AI-driven tasks from anywhere. Meanwhile, Microsoft made Copilot Cowork generally available worldwide on June 16, built in partnership with Anthropic to move beyond chat and into execution. The three products — ChatGPT Work, Claude Cowork, and Microsoft Copilot Cowork — now compete directly for the attention of enterprise IT departments and individual knowledge workers alike.The convergence is striking. All three products share a remarkably similar vision: a persistent AI agent running in the cloud that can break complex tasks into steps, connect to workplace tools via plugins, and produce finished outputs rather than just conversational replies. All three work across desktop, web, and mobile.What distinguishes OpenAI’s approach is its raw consumer distribution advantage. ChatGPT has reached 900 million weekly active users, and OpenAI now has 50 million paying subscribers. More than 9 million paying business users rely on ChatGPT for work, and 92% of Fortune 500 companies now use ChatGPT. By making ChatGPT Work available to Plus subscribers at $20 a month — not just Enterprise or Pro customers — OpenAI is betting that broad accessibility will drive adoption faster than any competitor can match.OpenAI’s product manager says AI is a partner, not a replacement — with a caveatWhen asked about the potential impact on the labor market, Geri was careful with his framing. He declined to speak broadly about workforce disruption but offered his personal experience as a product manager whose day-to-day work has been substantially reshaped by the tool.”My job is not to schedule bug bashes and find out who contributed to a specific feature. That’s a task I do in my job, but that’s not my job,” Geri said. “My job is to make an amazing product.” He described ChatGPT Work as “a partner” and “an extension of me, certainly not a replacement,” adding: “Everybody feels far more productive than before, but is also almost working harder than before, because you get to work on all the things you want to work on as opposed to the drudgery around it.”But Geri was also careful not to minimize the sophistication of the work the agent can handle. “I also don’t want to say that it’s only doing mundane tasks because, like something like hill climbing retention curves on a given feature is not mundane. It’s actually really hard to do,” he said. The distinction matters. If ChatGPT Work were merely automating calendar invitations and expense reports, it would be a convenience tool. The fact that Geri describes it compressing three months of analytical product work into a single week suggests something with far greater implications for how teams are structured and staffed.An IPO-bound company needs ChatGPT Work to prove enterprise AI can generate revenueThe timing of ChatGPT Work’s launch is impossible to separate from OpenAI’s IPO trajectory. The company needs to demonstrate that it can convert its massive consumer user base into durable enterprise revenue — a narrative that becomes significantly more compelling with a product explicitly designed around professional workflows.OpenAI said it is generating $2 billion in revenue per month, growing four times faster than Alphabet and Meta did at comparable stages, with enterprise now making up more than 40% of revenue and on track to reach parity with consumer by the end of 2026. But OpenAI remains heavily loss-making, and the company does not expect to reach profitability until around 2030, with internal projections suggesting losses of $14 billion in 2026 alone.The competitive dynamics are unprecedented. Anthropic filed for its own IPO on June 1 at a $965 billion valuation, setting up simultaneous public listings from the two most prominent AI startups in history. Whether both can sustain their lofty valuations under the scrutiny of public market investors will depend in large part on whether products like ChatGPT Work and Claude Cowork deliver measurable productivity gains to paying enterprise customers.The launch also caps a product trajectory that began with ChatGPT Enterprise in August 2023, accelerated through the release of OpenAI’s Operator agent in January 2025, and continued through Operator’s deprecation and shutdown on August 31, 2025, when its capabilities were folded into the ChatGPT agent framework. ChatGPT Work is the consolidation of those efforts into a single, unified product — one that pairs GPT-5.6’s three model variants (Sol for power, Luna for speed, and Terra for balanced everyday use) with a persistent cloud environment and an expanding library of MCP plugins.The future of work may already be running in the cloudWhen asked whether ChatGPT Work signals a shift toward a new kind of operating system — one where users interact with their computers primarily through an AI agent rather than through traditional mouse-and-keyboard interfaces — Geri stopped short of making sweeping predictions. But he hinted at the direction OpenAI sees ahead.”Anybody who has worked with Codex or now ChatGPT Work will realize how exciting it is to interact with your environment and your computer via the agent,” he said. “Especially in the desktop app, where the model has access to your entire machine and can interact with websites on your behalf — it’s really able to be an extension of you and a real partner, and that certainly feels like the future.”At the end of the interview, Geri circled back to something personal. “I’ve never enjoyed work as much as I have in the last month using ChatGPT Work and Codex,” he said — a striking admission from a product manager who, until recently, spent a meaningful share of his days clicking through the same interface five times in a row just to see if it would break. OpenAI is now asking 900 million users to believe that feeling scales. For a company weeks away from one of the largest public offerings in history, the answer to that question is worth roughly $850 billion.

Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research’s June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds.There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work).Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place.Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each.Expensive hardware is idle: 86% of GPU operators report utilization of 50% or lessEighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity.The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today. Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that’s an AI-specialized cloud contract, new accelerators, or more GPUs. Most deployed “agents” do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their ownSeventy-one percent of enterprises say a quarter or fewer of their deployed “agents” can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies.That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as “agentwashing.”Meanwhile, Zapier’s enterprise survey said 72% reported deploying or testing autonomous agents; and Writer’s 2026 survey has 97% of executives saying their company deployed AI agents in the past year. Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them. 66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evalsTwo-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision.The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents.And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime. This finding is explored in more depth in VentureBeat’s related coverage of the evaluation gap, which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks. 69% run credential sharing somewhere in the agent fleet — and those companies get hit far more oftenSixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity. The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems.57% traced a confident, wrong agent answer to their own missing or inconsistent business contextFifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once.Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven’t started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them.The quarter where agent technology “portability” became a priorityOne more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%. Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic’s Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2’s open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5’s price; and Tencent’s Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government’s review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven’t established a causal link here, the timing is worth noting.The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%.Five layers, no incumbents, 12 monthsThe synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider’s built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well. Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers’ offerings.Those defaults are winning on convenience, and they’re also what the coming spending decisions will test. The survey didn’t ask which direction that money moves — toward the platforms’ built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters.The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped.VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform, July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production. Disclosure: VentureBeat produces both this research and VB Transform

Enterprise AI teams are giving agents more freedom at the same moment their confidence in automated testing is collapsing.Half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations and yet still caused a customer-facing failure — one in four more than once — according to the June 2026 VB Pulse survey of 157 qualified enterprise respondents at companies with 100 or more employees.The sample is self-selected rather than a probability sample, so the findings should be read as directional, not precise.But enterprises are not responding by slowing automation: 66% of respondents already permit some production deployment without human review or are building systems intended to do so within the next 12 months. Only 5% say they fully trust the automated evaluations that would make those release decisions.That mismatch is the evaluation gap: the autonomy ceiling is rising faster than the assurance beneath it. It also fits a broader thesis that will be explored at VB Transform 2026: enterprises ship agents first, while the control layers around identity, evaluation, cost, context and orchestration are arriving later. The next year will be a retrofit cycle, with buyers shifting budget toward the systems that make agentic deployments governable and dependable.Why a passing evaluation is not a working agentTraditional software testing usually asks whether a defined input produces an expected output. Agent testing is harder because the system may choose its own sequence of steps, call tools, retrieve data, alter state and respond differently from one run to the next.An agent can make several individually plausible decisions and still reach the wrong result. It may retrieve the correct account but update the wrong field. It may draft a valid refund request but send it without approval. It may call five tools successfully before a sixth step leaks sensitive information or leaves a workflow incomplete.The survey shows enterprises already recognize this limitation. The most common reason for distrusting automated evaluation is poor alignment with real-world outcomes, cited by 29% of respondents. Bias or inconsistency follows at 21%, lack of explainability at 18%, and data leakage or privacy concerns at 17%.That hierarchy matters. Enterprises are saying the score often does not predict what happens when a customer, employee or business process encounters the agent in production — not that automated scoring is too slow or expensive.NIST makes a similar point in its Generative AI Profile: measurements gathered in controlled environments may not transfer cleanly to deployment because behavior changes with prompts, users, context and operating conditions. Its guidance calls for field testing, post-deployment monitoring and clear processes for escalating failures.Capability is not consistencyA single successful run proves that an agent can complete a task. It does not prove that it will complete the task reliably.Anthropic’s guidance on agent evaluation distinguishes between measuring whether a system succeeds at least once across repeated attempts and whether it succeeds every time. That distinction is essential for customer-facing or operational workflows. A model that occasionally produces an excellent answer may still be unacceptable if the same task fails unpredictably on the next attempt.Enterprise teams should therefore treat repeatability as a first-class metric. That means running the same scenario multiple times, varying phrasing and context, testing tool failures, and measuring whether the final business outcome remains correct even when the route changes.The evaluation set also has to evolve. Every production incident should become a permanent regression test. Customer escalations, failed tool calls, incorrect approvals and data-handling mistakes should feed back into the pre-deployment suite rather than remaining isolated support cases.Autonomy should expand by risk, not by ambitionThe survey does not imply that every agent action should require a person. Human review cannot scale across millions of low-consequence decisions.But zero-human operation should be earned by demonstrated reliability and bounded by the consequences of failure.Low-risk actions such as drafting internal summaries or categorizing documents can tolerate broader autonomy. Financial transactions, customer communications, code deployment, access-control changes and data deletion need stricter thresholds, repeated consistency tests, policy checks, rollback mechanisms and clear human escalation paths.The risk isn’t evenly distributed by company size, either. Larger enterprises — those with 2,500 or more employees — are moving toward zero-human deployment fastest, at 70% versus 64% for smaller companies, and they’re also shipping more agents that go on to fail a customer, at 54% versus 48%. That is the warning for enterprise leaders. Removing the human from the loop does not remove uncertainty. Without stronger assurance, it converts uncertainty into an automated production decision.The market will keep pushing toward greater autonomy because the economic incentive is real. The organizations best positioned won’t be those that remove people fastest — they’ll be the ones that treat repeatability and regression testing as seriously as deployment speed.

The vast majority of business data is tabular — living in data warehouses, CRMs, and financial ledgers — yet building a reliable model from it still means training a new one from scratch for every dataset, then maintaining hyperparameter tuning loops, feature engineering, and retraining pipelines to fight data drift. Google Research is proposing a way around that: a new foundation model called TabFM that treats tabular prediction as an in-context learning problem instead.It can generate predictions for a new, unseen table in a single forward pass. For enterprise developers and AI engineers, this reduces the time-to-production from weeks of pipeline engineering to a single API call.The challenge with traditional MLTo extract reliable predictions from a gradient-boosted tree, data scientists must build and maintain complex data pipelines. They have to clean messy inputs, impute missing values, encode categorical variables into numerical formats, and engineer custom feature crosses.Once the data is ready, they must run repetitive hyperparameter optimization loops, searching across learning rates, tree depths, subsampling ratios, and regularization grids to find the best configuration. Once deployed, these traditional models “incur ongoing operational debt through data drift monitoring and retraining pipelines to stay accurate,” Weihao Kong, Research Scientist at Google Research, told VentureBeat.Meanwhile, the rest of the AI industry has moved on. Generative AI models for text and computer vision have seamlessly shifted to zero-shot inference, where a model can perform a completely new task simply by being prompted with context. Large language models (LLMs) already excel at in-context learning, so why can’t we just feed tables into an off-the-shelf LLM?Because LLMs are trained on natural language rather than structured data, they struggle to process tables directly. First, their context limits are exhausted quickly by medium-sized tables containing just a few thousand rows and hundreds of columns. Second, LLMs suffer from tokenization inefficiency, awkwardly splitting numerical values and destroying mathematical precision. Finally, they suffer from structural blindness. When a 2D table is serialized as a 1D text string, LLMs lose track of which value belongs to which row and column as the table grows. “That’s why, today, it is far more effective to use an LLM to write the code that handles feature engineering and calls XGBoost than to ask the LLM to read the table itself,” Kong said.What is TabFM?To run inference with TabFM, you do not update any model weights. Instead, you take your historical examples (the training rows with their known labels) and your target rows (the new data you want to predict) and pass them to the model as a single, unified prompt. The model learns to interpret the relationships between columns and rows directly from this context at runtime.For example, consider an enterprise analyst trying to predict customer churn. Instead of building a bespoke data pipeline and training an XGBoost model, they can simply pass a sample of historical user session data alongside a new, active session into TabFM. In one forward pass, the model returns an instant churn probability. TabFM overcomes the limitations of LLMs by treating the data as a grid, preserving its structural integrity without forcing it into a single-dimensional text string.To effectively process diverse tabular structures while enabling scalable zero-shot prediction, TabFM synthesizes the strengths of earlier experimental architectures, TabPFN and TabICL. TabPFN, developed by Prior Labs, first proved that a transformer architecture could perform zero-shot classification on small tables, though it struggled to scale computationally to larger datasets. Later, TabICL, developed by France’s National Research Institute for Digital Science and Technology, addressed this bottleneck by introducing row compression, allowing in-context learning to efficiently process much larger tables. TabFM combines TabPFN’s deep feature contextualization with TabICL’s efficient compression into a novel hybrid design built on three key mechanisms:1. Alternating row and column attention: The raw table is first processed through a multilayer attention module that alternates across both columns (features) and rows (examples). By continuously attending across these two dimensions, the model natively captures complex feature interactions. This deep contextualization does the heavy lifting that would usually require tedious manual feature crafting by data scientists.2. Row compression: Following this contextualization, the cross-attended information for each row is compressed into a single, dense vector representation. TabICL pioneered this by using CLS tokens to compress a row’s rich information into one vector, “in contrast to TabPFN v2, v2.5, and v2.6, which attend over the full cell grid throughout the network,” Kong explained. This drastically shrinks the computational footprint.3. In-context learning (ICL): A causal Transformer then operates on this sequence of compressed embeddings. This Transformer model uses the attention mechanism of TabICL to attend over these dense row vectors, drastically reducing the computation cost and allowing the model to process large datasets efficiently.A major selling point of TabFM is its pretraining recipe. The model was trained entirely on hundreds of millions of synthetic datasets. These datasets were dynamically generated using structural causal models (SCMs) that incorporate a wide variety of random functions. By training exclusively on synthetic SCMs, TabFM learned the fundamental mathematical priors of how tabular features interact without ingesting real-world, confidential CSV files.TabFM in actionTo test the model’s capabilities, Google researchers benchmarked TabFM on TabArena, a comprehensive evaluation suite spanning 51 diverse tabular datasets across 38 classification and 13 regression tasks.On these public benchmarks, TabFM’s zero-shot predictions already match or beat heavily tuned supervised baselines. However, Google is careful to note that this does not automatically mean TabFM will universally dethrone bespoke, hyper-optimized production models on every enterprise workload.”Instead of replacing hyper-optimized production models, the true practical business value it unlocks for lean engineering teams is velocity,” Kong said. “It allows data analysts and backend engineers to instantly spin up high-quality baseline models without a dedicated data science team managing a complex lifecycle.”For advanced practitioners looking to squeeze out maximum accuracy, the research team also introduced a “TabFM-Ensemble” configuration. By running the model through 32 distinct variations and blending the results, TabFM pushes the performance even further. Getting started, trade-offs, and the cloud futureThe shift to in-context learning for tables introduces a new economic trade-off that engineering teams must consider. With traditional algorithms, training is slow and expensive, but inference is lightning-fast and cheap. TabFM flips this dynamic. While training time drops to zero, inference becomes significantly heavier. Because the model must process the entire historical dataset as context during every single prediction, it requires more compute and memory at runtime. In this new paradigm, “traditional machine learning training becomes the ‘prefill’ phase (KV caching) in the context window,” Kong said. While this prefill cost is steep, it is paid only once per table, and the cache is reused across subsequent queries. “The catch is prediction latency, which no amount of caching removes,” Kong added. Every new prediction requires a pass through a large transformer. “Any production API requiring single-digit-millisecond response times cannot tolerate TabFM’s forward-pass overhead.”For developers looking to evaluate the model today, the barrier to entry is low. Google designed TabFM as a drop-in replacement for traditional ML workflows, offering a scikit-learn compatible API (TabFMClassifier and TabFMRegressor). It natively handles mixed numerical and categorical columns, works directly with pandas DataFrames, and requires no manual ordinal encoders or numerical scalers. The library supports both JAX and PyTorch backends.However, enterprise teams need to be aware of current limitations and licensing restrictions. The model architecture has a hard limit of 10 output classes for classification tasks, and it is optimized for tables with up to 500 features. More importantly, while Google released the underlying codebase under the permissive Apache 2.0 license, the pre-trained model weights are published on Hugging Face under a strict tabfm-non-commercial-v1.0 license. Developers can evaluate the model internally, but it cannot be deployed in commercial products yet.Looking ahead, Google is addressing the commercial deployment friction through its cloud ecosystem. TabFM is being integrated directly into Google BigQuery, allowing analysts to run zero-shot predictions natively via an “AI.PREDICT” command. By putting foundation model inference right next to the data warehouse, TabFM could soon make complex tabular machine learning as accessible as a basic database query.In practice, TabFM shines in rapid prototyping, high data drift environments, and small to medium-sized datasets under 100,000 rows. Conversely, teams should stick to traditional models for strict, ultra-low latency APIs, or massive tables exceeding one million rows, which currently require aggressive row sampling that degrades the foundation model’s competitive advantage.

Share one API key across five AI agents, and a single compromised agent inherits the reach of all five. The attacker immediately benefits from the accumulated permissions of every workflow that the key touches. The forensic trail goes cold at the credential level because five agents on one account leave no record of which agent did what.Sixty-nine percent of enterprises run agents with credential sharing somewhere in their deployments, according to VentureBeat’s June 2026 Pulse Research wave of 107 enterprises. That one number explains the buying spree reshaping enterprise security this year. Palo Alto Networks, CrowdStrike, and Cisco have collectively bet more than $22 billion on it in the past year, targeting exactly the layer most enterprises in this survey haven’t finished building. Palo Alto Networks completed its acquisition of CyberArk on February 11 for $21.1 billion in total consideration at close — a deal it announced last July at roughly $25 billion and the largest in the company’s history.CrowdStrike closed its $740 million acquisition of runtime authorization platform SGNL and, by June 15, shipped the first product from the deal, Continuous Identity for AI Agents. CrowdStrike integrated SGNL in less than a year, delivering a product that validates every agent action in real time based on who owns it, who is calling it, and the device’s risk posture.Cisco announced its intent to acquire non-human identity specialist Astrix Security on May 4 for a reported $400 million.For a security director, this survey reads as a board-level question, not a trend line. It also surfaces a finding no competitor’s data shows, one that exposes which companies are the most at risk.The data below is the first look at VentureBeat’s Q2 Agentic Security report, drawn from 107 qualified respondents at organizations with more than 100 employees. The full report will be released to attendees at VB Transform, the event in Menlo Park next week (July 14-15) focusing on enterprise autonomous agents. Forty-five percent are final decision-makers for AI purchases. The sample skews mid-market, so read the numbers as the view from organizations adopting agent security right now rather than from the largest enterprises. More than half of respondents, 54%, have already had an agent security incident or near-incident. Eighteen percent confirmed an incident, and thirty-six percent caught a near-miss before a breach. Security teams are stopping most of these events at the last control point in the chain, but the rest of the data shows how thin that margin is.Your agents are sharing credentialsOnly 32% of enterprises give every AI agent its own scoped, managed identity. Nearly half (48%) report that some agents have scoped identities, while many still share credentials. Another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. The survey question allowed more than one selection, and 24 of the 107 respondents chose multiple options — which is why the three categories sum to 112%. Deduplicated by respondent, 74 organizations, or 69%, flagged credential sharing in at least one answer.One number explains why the acquisitions target this layer. A shared credential converts a single compromised agent into many, and CyberArk’s research puts machine identities at 82 for every human in organizations worldwide, with agents as the fastest-growing category of the ratio. Cisco made the same diagnosis when it bought Astrix, whose founders built the company around API keys, service accounts, and OAuth tokens. Cisco’s announcement calls those the credentials AI agents are now “using (and abusing)” to execute work at scale.Adam Meyers, senior vice president of counter adversary operations at CrowdStrike, described the mechanism directly in an interview with VentureBeat. Some AI systems have their own identities, he said, and in other cases “people give their identity to the AI to take action on their behalf, and that also further kind of murkies the water and makes it very complex.” The murk is the point, because when the identity is shared, attribution dies with it.Exposure scales with size, and containment does notForty-nine percent of enterprises enforce scoped permissions at runtime, and 47% monitor and log agent activity, which can help reduce security incidents. Only 30% sandbox their highest-risk agents, the one control that limits blast radius when the first two fail. Isolation is what keeps a single compromised agent from becoming a deployment-wide event. Enterprises have funded detection and resistance, but the containment layer barely exists.The sharpest finding in the survey, and the one no vendor report captures, shows up when you split results by company size. The incident rate is 49% for companies with 101 to 1,000 employees, but it shoots up to 63% for companies with more than 1,000. Sandbox isolation moves the other way, falling from 35% to 20% at the larger companies.The chart above shows the same finding at finer granularity: the 49%/63% split above is a binary cut at 1,000 employees, while the bars here break incident rate and isolation rate into four size bands. The red line measures incidents and near-misses, and the navy tracks the one control that contains damage after everything else fails. At organizations with 101 to 250 employees, the two sit 7 points apart, but above 5,000, the gap blows out to 60 points. That top band pools the survey’s two largest size groups and holds only 15 respondents, so treat the number as directional. Larger enterprises run more agents across more systems, which drives incidents up while sandboxing, the engineering project that would contain them, goes unfunded. The enterprises with the most agents have the least isolation around them.The deals target exactly those accounts. Palo Alto Networks, Cisco, and CrowdStrike sell to large enterprises first, where incident rates are highest and containment is the thinnest.Guarded by whoever shipped the modelThe model providers are the security layer. OpenAI’s built-in guardrails lead at 51%. Google Cloud reaches 36%, Microsoft Azure’s Purview and Copilot Studio DLP 35%, and Anthropic’s managed-agent controls 29%. Eighty-two percent of respondents name a provider-native or hyperscaler control as their single primary agent security layer.The purpose-built specialists are in single digits, with Palo Alto Networks’ Prisma AIRS at 7%, CrowdStrike at 6%, and Okta for AI Agents at 4%. Zenity and the dedicated non-human identity platforms are at 3% each. Microsoft Entra Agent ID is the highest-penetration identity-specific control in the dataset at 13%, the only one from a hyperscaler, and it still falls outside the top four. Only 5% of enterprises run no dedicated agent tooling at all, and the rest have tooling that came pre-installed.Bundled controls lead because they ship free and are enabled by default. Most filter prompts and outputs, but they do not give an agent its own identity or sandbox it. Hyperscalers sell identity-layer products, and Entra Agent ID is in the dataset at 13%, but adoption stays low. The two controls that reward incident data the most, scoped identity and isolation, are the two that the default stack does not include.Prompt-and-output filters evaluate whether a call looks malicious. That is an intent problem, and intent cannot be solved at the language layer. CrowdStrike CTO Elia Zaitsev drew the line in an interview at RSAC 2026. “Observing actual kinetic actions is a structured, solvable problem,” Zaitsev said. “Intent is not.” CrowdStrike’s Falcon sensor walks the process tree on an endpoint and tracks what agents did, not what agents appeared to intend. A scoped identity and an isolation boundary give that sensor something to track, while a shared credential on a bundled guardrail does not.Cloud security went through the same cycle a decade ago, and Palo Alto Networks, CrowdStrike, and Wiz built multi-billion-dollar businesses on the gaps native cloud controls left open. Agent security is tracking the same path faster. A misconfigured storage bucket sat open until a human noticed. A misconfigured agent exploits its own over-permissioning on every run, and no human is watching when it does. Merritt Baer, chief security officer at Enkrypt AI and a former deputy CISO at AWS, told VentureBeat that the default layer is thinner than enterprises assume. “Enterprises believe they’ve ‘approved’ AI vendors, but what they’ve actually approved is an interface, not the underlying system,” Baer said. “The real dependencies are one or two layers deeper, and those are the ones that fail under stress.”Comfortable, unconvinced, and already shoppingHere is the contradiction worth a keynote slide. Enterprises rate their agent security tooling 4.2 out of 5, with value for money at 4.1 and ease of implementation at 3.9. Those scores would make most SaaS vendors envious.Only 35% believe their AI-enabled defenses are ahead of AI-enabled attackers, while thirty-two percent call it roughly even. Twenty-one percent say attackers lead, and another 21% say it is too early to tell, showing how enterprises trust their tooling more than they trust its outcomes.Budgets confirm it. Forty-six percent allocate 6 to 10% of the security budget to agent security, and a full third spend 5% or less. Half the sample has already had an incident or near-miss, but the funding does not match the exposure.Fifty-nine percent plan to adopt, add, or replace agent security tooling within 12 months, and twenty-nine percent plan to move this quarter. OpenAI leads forward interest at 34%, followed by Google at 30%, Anthropic at 29%, and Azure at 25%. The dedicated vendors draw more interest looking forward than their current single-digit footprint suggests. Satisfied customers do not reshuffle this fast unless they know the stack they’re currently using is provisional.Three moves for security directors 1. Inventory every agent’s credentials this quarter. Map which agents share credentials with other agents and which run on borrowed human or service-account identities. The goal is not one credential per agent. Agents that touch multiple systems need multiple scoped identities. The goal is zero shared credentials between agents and zero borrowed human identities. Thirteen percent of surveyed enterprises already run Microsoft Entra Agent ID. Okta for AI Agents and the non-human identity specialists sell equivalents. Shared and borrowed credentials are the first thing to eliminate.2. Sandbox the riskiest agents first. Isolation is the least-adopted control at 30% and the only one that contains blast radius after prevention fails. Rank agents by the sensitivity of what they touch and isolate the top of the list. Above 1,000 employees, where isolation falls to 20%, this is the single highest-return move in the dataset. Sandboxing does not require replacing the agent or the platform. It requires a policy decision and an isolation layer.3. Match the budget to the incident rate. A third of enterprises fund agent security at 5% or less of the security budget, even though more than half have already had an incident or near-miss. Nine percent allocate more than 25% today. The full report breaks out exposure and containment by company size, showing which bands carry the most risk and the least protection.The board’s question is simpler. If one of our AI agents was compromised this afternoon, which systems did it touch, and whose credentials was it holding? For the 69% of enterprises running agents on shared credentials, the answer is a shrug. The trail goes cold at the key.The full Q2 Agentic Security report, with the complete vendor matrix, industry cuts, and the full dataset behind these charts, debuts July 14 and 15 at VB Transform, held at Hotel Nia in Menlo Park. The open question it leaves is whether enterprises close the agent security gap on their own terms, or whether a confirmed breach closes it for them.

A team routing queries across a coding specialist, a logic specialist, and a generalist model assumes each will cover the others’ blind spots. A new study evaluating 67 frontier models from 21 providers shows that assumption is mathematically flawed — and the flaw has a name: the co-failure ceiling.The assumption works like this: as long as two models don’t usually fail on the exact same prompts, combining them is supposed to create a safety net against failures.The real limit on orchestration is not how often models disagree, but the percentage of prompts where every model in the pool gives the wrong answer at once. By ignoring the co-failure ceiling, enterprises are building complex, expensive routing infrastructure to chase performance gains that do not exist. Fortunately, developers can use this same math to build a cost-free test that determines exactly when multi-model orchestration will actually pay off.The hidden costs of the multi-model strategyTo orchestrate multiple language models, developers typically rely on three architectures. Model routers act as traffic cops, sending complex queries to expensive models and simple queries to cheaper ones. Cascades send every prompt to a cheap model first, only escalating to a premium model if the initial system signals low confidence. Finally, approaches like Mixture-of-Agents (MoA) fuse multiple models by asking them the same question and generating a synthesized answer from their combined outputs.These architectures introduce a “shadow price” to inference costs. Every time a development team implements a router or a cascade, they pay a premium in added system latency, complex infrastructure maintenance, and increased governance risks across multiple API providers.To justify these operational costs, engineers rely on “pairwise error correlation” to select their model pool. Imagine a developer has Model A, which writes excellent Python but fails at SQL, and Model B, which writes excellent SQL but fails at Python. Because they fail on different types of prompts, their pairwise error correlation is low. The developer assumes that by placing a routing layer in front of them, they have created a composite system that rarely fails at coding.According to the study, throwing diverse models together based on low correlation can actually hurt performance if the models are not equally capable — when you vote across diverse but unequal models, the weaker ones often gang up and outvote the smartest one.Josef Chen, author of the paper, told VentureBeat that in their experiments, “Naive majority voting across unequal models had negative mean gain (minus 10 points on our hard mix): diverse-but-weaker members outvote the strong one.” The actionable advice for developers is to “combine only models within a matched quality band.” If you cannot match quality, take the single-model baseline and spend your budget on the best model available.The paper provides one bright spot for this approach regarding MoA architectures. When building ensembles, teams often use “Self-MoA,” where they query the same premium model multiple times to generate a synthesized answer. The researchers found that at matched quality, building a diverse ensemble of models with low pairwise correlation beats a high-correlation Self-MoA setup.However, when teams use that same pairwise correlation metric to predict the absolute accuracy of their overall system, the math breaks down.”So teams pay the orchestration overhead up front (latency, complexity, multi-provider operations) on the assumption that a diversity dividend arrives later,” Chen said. “Usually it doesn’t, because today’s best models agree, and, worse, they fail on the same queries … the prompt simply carries little signal about which model will be the one that’s right when the frontier disagrees.”Why the math fails: the co-failure ceilingThe core finding of the study centers on a metric called the “co-failure rate” — the formal name for the all-wrong scenario described above. No router, voting system, or cascade can ever achieve an accuracy higher than the ceiling it imposes.The coding, logic, and generalist pool shows low pairwise correlation on routine prompts — they rarely fail together. But the co-failure ceiling represents the obscure, highly complex edge case that pushes past the limits of current AI architectures. If a prompt is so difficult that all three models hallucinate or fail, it does not matter how intelligently the router distributes the task. The entire pool wipes out at once.The researchers tested their 67-model pool, which included GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro, on the open-ended MATH-500 math benchmark. Based on standard pairwise correlation, statistical models predicted that the entire pool would wipe out simultaneously on only 2.3% of the questions. In reality, the co-failure rate was 5.2%.Standard correlation metrics underestimated the failure rate by roughly 2.25 times. The culprit is not just independent difficulty, but a shared failure point.”The driver is what we call a common-mode atom: a slice of queries on which the entire market fails together, which no pairwise statistic can see,” Chen said. “Adding a 20th model to your pool doesn’t buy tail coverage. The tail is shared.”The researchers also found that task format directly triggers co-failure. When they took graduate-level science questions from the GPQA benchmark and changed them from multiple-choice to free-response formats, the all-wrong tail expanded to 12.7%.Developers can engineer around the ceiling, though. “The engineering implication is uncomfortable: multi-model setups buy the least exactly where teams want them most, on open-ended generation,” Chen said. “Anywhere you can convert generation into verification or constrained selection (structured outputs, checkable answers, execution tests), you reopen the ceiling.”Ultimately, the researchers found this ceiling limits AI applications in two distinct ways, depending on the domain:Ceiling-bound environments (e.g., open-ended math): The co-failure rate is high. The task is too hard, and all models fail simultaneously. No amount of routing can bypass the lack of underlying capability.Realizability-bound environments (e.g., graduate-level science): The co-failure rate is near zero, meaning at least one model in the pool usually knows the answer. However, the models disagree so subtly that a routing layer cannot reliably pick the correct answer without an omniscient oracle.The $0 pre-deployment sanity checkBefore dedicating engineering hours to building a router, teams can calculate their absolute performance ceiling for free using a mathematical formula called a Clopper-Pearson bound.The Clopper-Pearson bound operates as a worst-case scenario calculator. If you flip a coin ten times and get eight heads, you cannot guarantee the coin will land on heads 80% of the time forever. The bound takes a small sample of test questions and outputs a mathematically guaranteed ceiling.Applied to language models, suppose a team tests a pool of five agents on 50 sample queries and finds they all fail together on just two questions. A developer might assume their multi-agent system will achieve 96% accuracy in production. The Clopper-Pearson formula corrects this optimism. It analyzes the small sample size and provides a mathematical guarantee that the true co-failure rate could actually be as high as 12%.To use this in practice, enterprises must build a held-out dataset. A fintech company, for example, could take 200 complex customer support tickets from the previous quarter and have human agents write perfect resolutions to serve as a benchmark. While this sounds like a heavy manual project, mature engineering teams can automate the entire ceiling calculation.”Integration is trivial: it’s a counting job over eval logs teams already produce,” Chen notes, “so it runs in the same CI stage as the eval suite and re-triggers whenever the model pool or the workload changes.”The engineering team then runs its candidate models against these 200 tickets once and records the results. When they want to evaluate multi-model configurations, they can use the co-failure rate measure to predict the maximum accuracy they can get from the system without running extra queries.One important conclusion the study draws is that on tasks where answers can be definitively checked, combining models rarely beats using the single best model on the market, unless the team possesses an exceptionally strong query-level routing signal.In an enterprise environment, a definitively checked task has an objective, zero-tolerance answer. This includes generating a SQL query that must execute without error, extracting a specific invoice total from a 50-page PDF, or formatting a JSON payload that perfectly matches a strict schema. For these tasks, enterprises are usually better off paying a premium for the smartest frontier model rather than weaving together three cheaper models and hoping a router picks the correct output. The study didn’t test subjective, ungraded tasks like drafting marketing copy — the authors note that whether these findings hold outside their verifiable benchmarks remains an open question.Because this mathematical check is free, enterprise teams can track their own co-failure rates as new models drop.”The measurement costs nothing, so any team can track its own co-failure rate across model generations and watch whether the tail is closing,” says Chen. Ultimately, “the lever buyers hold is failure-mode heterogeneity and market churn, not model count.”

Presented by SAPGenerating code with AI is fast, but getting that code to run reliably inside a large enterprise, integrated with live systems, governed for compliance, and maintainable over years requires foundational work that most organizations underestimate. While 81% of all organizations have a detailed strategy, only 12–16% reach AI‑driven execution, says SAP’s Michael Ameling, CPO of SAP Business Technology Platform, and the reasons rarely come down to the quality of the generated code.”Across industries, enterprises that have invested heavily in AI tooling are hitting a wall when generated code meets the reality of their existing environments, because generating code and operationalizing it are not the same problem,” Ameling says. There are specific requirements for deploying AI-generated logic at enterprise scale: what data and integration readiness actually look like, how governance works when AI agents move from producing recommendations to executing workflows, and how development teams are changing their role as AI takes over more of the coding work.Why AI code generation fails in enterprise production environmentsThe productivity gains from AI code generation are real and well-documented, but the ease of prototyping has given many organizations a misleading sense of how far along they actually are. “Generating code is one thing,” Ameling says. “Enterprise customers, including multinationals and large organizations, need to ensure there are no compromises in compliance or security. Code that runs reliably for ten or twenty years, as it does at many of SAP’s largest customers, also has to be maintained, patched, and understood by whoever inherits it. Life cycle management, in other words, does not generate itself.”The issue is rarely the generation quality. Teams build something compelling, then discover they lack access to the data it depends on, or the integrations it assumes, or the permissions required to run it in a real environment. The problem is essentially that AI amplifies an organization’s existing data and process maturity, but it can’t substitute for it.This dynamic intensifies as AI moves from producing code to executing actions. Latency, cost, and system load all increase when logic runs continuously against live data rather than rendering a one-time output. The performance requirements of an autonomous agent operating across a multinational’s transaction systems are categorically different from those of a developer copilot.How to connect AI-generated logic to fragmented enterprise systemsThe architecture challenge that most enterprise AI projects underestimate is integration. Real enterprise environments are not clean slates: they combine cloud systems, legacy on-premise infrastructure, fragmented data stores, and dozens of business applications that were never designed to talk to each other. Getting AI-generated logic to operate reliably across all of them requires a layer that unifies data access, process context, and governance, and it has to be in place before any agent starts executing. And organizations that see AI as a reason to defer infrastructure modernization are making a mistake. “The question is not whether to modernize or not. Of course you need to modernize,” Ameling says. “But the value you get on top of this is much higher with AI. Federated data access and harmonized process layers are not alternatives to upgrading a fragmented landscape, they’re what make the upgrade worthwhile.”At the platform level, this translates into a set of practical requirements: structured data integration, end-to-end process visibility, and the ability to discover and connect to APIs across both modern and legacy systems. SAP’s approach with the Business AI Platform draws on tools including its Joule Studio, Integration Suite, Business Data Cloud, and SAP AI Agent Hub enterprise architecture layer to provide that context. The goal is to give AI-generated logic accurate, current knowledge of what a business is doing and how, rather than just access to raw data.AI agents handle large challenges by dividing them into smaller, autonomous tasks, with each agent responsible for a specific domain, and all coordinated toward a shared outcome. A financial close, for example, involves dozens of discrete sub-processes. Agents handling each task in parallel, within defined constraints, can compress cycle times dramatically, but only if the underlying systems they interact with are coherent and accessible.The governance and oversight that AI agents require in productionWhen AI moves from assistant to operational actor, the governance questions loom large, because agents that trigger workflows, update records, and interact with live business systems need the same accountability framework that applies to human employees, i.e., identities, defined privileges, and auditable behavior.There are two distinct models:Principal propagation, where an agent acts on a user’s behalf, inheriting that user’s permissions and scope.System-triggered agents, where the agent operates under its own identity and role-defined privileges, functioning more like an automated HR role than a personal assistant.Both models require the same underlying infrastructure: an agent hub where operators can see which agents exist, what APIs they can access, and what they are authorized to do. Observability also needs to be operationalized correctly for AI, combined with both technical and business evals. “In production, openness is very important,” Ameling says. “We use OpenTelemetry as a framework, so we can integrate with other solutions, for end-to-end observability of the tool, third-party agents and the like.”On top of that, standard technical evals, which test whether an agent produces consistent outputs, are necessary but not enough. Business evals assess whether an agent is actually moving the performance indicators it was deployed to improve, but it has to work end-to-end.Where the testing happens is equally important. The traditional software development cycle across dev, test, and production environments breaks down when a model produces different outputs depending on whether it is running against test data or live data. Getting to trustworthy AI in production means accepting that validation looks fundamentally different from what engineering teams have practiced for decades, with live environment testing, even A/B/C testing to ensure outcomes are reliable.How AI-driven code generation is changing software engineering rolesThe role of the developer is not disappearing in this environment, but its center of gravity is shifting. The productivity multiplier is significant when developers can run multiple coding agents in parallel across open terminals, each working on a separate problem and each taking several minutes to complete. But it introduces a new kind of cognitive demand, because humans have to stay in the loop. That means tracking context across concurrent workstreams, evaluating outputs that range across large codebases, and making architectural judgments that no agent can be trusted to make alone.”The more specific and complete the prompt, the less intervention is required, and developers are learning that bringing more context upfront pays dividends in reduced back-and-forth,” Ameling says. “But the output still needs to be understood, not just accepted.”The competitive edge will remain intellectual property, not tooling. The companies that pull ahead will be those that most effectively encode their domain knowledge into the systems they build.”A manufacturer’s process expertise, a financial institution’s risk logic, a logistics firm’s routing intelligence, these are the assets that AI can accelerate, but only if the organizations that hold them do the work to make them accessible and usable,” Ameling says. “Protect that, and apply AI to accelerate your differentiation.”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.