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A joint research collaboration between researchers at the University of Illinois at Urbana-Champaign (UIUC), UC Berkeley, and the open source AI-native vector database platform Chroma unveiled Harness-1, a 20-billion parameter open-source search agent built atop OpenAI's gpt-oss-20B open source model that fundamentally redesigns how AI executes complex retrieval tasks. Harness-1 achieves a massive leap in performance, scoring 73% average on its ability to recall relevant information correctly from a curated dataset, outperforming even GPT-5.4 (70.9%) and the next, most accurate open source search agent, Tongyi DeepResearch 30B, by 11.4 percentage points. (While

In Q1 2026, VentureBeat's Pulse Research surfaced the “Governance Mirage”: the gap between the governance org charts enterprises had drawn and the control layers they had actually built. Forty-three percent said a central team owned AI governance; 23% couldn't agree on who owned it at all; and 31% named vendor opacity as the single biggest obstacle.This new wave of research asks the next question: Once you've admitted the governance problem, what breaks first when you try to fix it? The answer from our respondents is unambiguous. The failure point is not the model. It's the runtime.Enterprises are discovering that AI agents built on stateless infrastructure — Python scripts, LangChain chains, ad hoc orchestration — cannot survive the operational realities of production. Container restarts er

Our system did one thing, and it did it well: It turned natural-language questions into API calls.The users were analysts, account managers, and operations leads. They knew what data they needed, but assembling it manually meant pulling from four dashboards, two BI tools, and a Salesforce report builder. With our system, they typed the request in plain English. A request like "Compile a report on sales volume for January through March 2026 for the Northeast region, broken down by city" was translated into an API call that the system could act on:json{  "description": "User requested sales volume for the given date range, here is the API call to get the response",  "api_call": "/api/sales_volume",  "post_body": {    "start_date": "2026-01-01",    "end_date": "2026-03-31",

Agentic AI is now a core part of the engineering process, driving massive execution leverage and helping us generate more code than ever before. Yet, a difficult question I’ve increasingly heard from business leaders is: if we’re shipping code faster than ever, why aren’t our products improving at the same rate?The reason is that writing code was never the rate limiter. Defining the right requirements, integrating with complex systems, and maintaining software under real-world conditions has always been the hard part. And when agents flood an organization with lots of new code, the hard part only gets harder. Agents compress execution time. They do not compress ambiguity, accountability, or operational complexity. As AI-generated code scales, human review is becoming a massive new bottleneck, and engineers are losing the context needed to catch agent mistakes. The companies that understand this will move forward deliberately and

For three years, Microsoft's artificial intelligence story has been inseparable from OpenAI. The partnership — cemented by a cumulative investment exceeding $13 billion — gave Microsoft early access to the most advanced AI models on the planet, catapulting its Copilot products into the enterprise mainstream and adding hundreds of billions of dollars to its market capitalization. To the outside world, Microsoft's AI strategy was OpenAI.Mustafa Suleyman wants to change that narrative.In an exclusive sit-down interview with VentureBeat at Microsoft Build 2026, the CEO of Microsoft AI disclosed that a contractual change with OpenAI roughly six months ago granted his division the formal authority to pursue what he openly calls "superintelligence" — using Microsoft's own researchers, its own data pipelines, and its own custom silicon."We

Microsoft used its Build 2026 conference this week to push a clear message: agents are rapidly moving into production throughout enterprise systems, and the winning platform will be the one that gives them reliable context, governance, identity, memory — and secure access to enterprise data. The company announced Microsoft IQ as a context layer across GitHub Copilot, Microsoft Foundry and Copilot Studio; Work IQ APIs coming June 16; Fabric IQ for structured business data; Foundry IQ for retrieval across enterprise knowledge and the live web; and Web IQ as a new agent-facing web search stack. Microsoft also introduced Scout, a personal work agent

When someone on a team corrects an AI agent — better prompts, better feedback, better context — that improvement disappears the moment a colleague opens the same tool. The correction doesn't transfer, and the next person starts from zero.The problem compounds in multi-agent workflows, where teams expect agents to share context across users and tasks. Without a shared memory layer, every team member effectively trains a different version of the same agent — and those versions never sync.That gap shows up in the numbers. According to Asana's own research, 75% of knowledge workers use AI on the job, but only 5% of companies have reported productivity gains. “Model providers are getting really, really good at improving reasoning and retry loops, but what they’re not good at is bringing the enterprise work context in a way that human beings can reason about for shared memory,” Asana Chief Product Officer Arnab Bose told VentureBeat. Asa