Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That's the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly? Add a second agent to a system, and you've added one connection. Add a tenth, and you haven't added ten connections, you've potentially added dozens, because now any agent might call any other, and each of those calls can trigger a call somewhere else. Complexity doesn'…
Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it? These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions. Consider a simple rule: Never open the car door. Followed literally, an agent could never get…
Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications. "In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems," Anand says. "As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration." That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI s…
Rob Strechay, until recently managing director and principal analyst at theCUBE Research, has joined VentureBeat as our first Lead Analyst and a founding analyst of VentureBeat Research. His arrival is the next step in a deliberate move at VentureBeat toward deeper specialization: analysis built for the technical decision-makers — the directors, VPs, CIOs, and CTOs — who are evaluating, buying, and deploying enterprise AI. The enterprise AI stack is being rewritten in real time, and the decision-makers I talk with are starved for objective, defendable data. Rob Strechay has the mix of technical rigor and operating experience needed to dissect the architecture behind the next phase of enterprise AI deployment. The questions enterprise technology leaders are asking have changed. As organiza…
A VentureBeat Pulse survey of 107 enterprises finds that over half have experienced an AI agent security incident or near-miss. Only a third give each agent its own identity, and most still share credentials. Only three in ten isolate high-risk agents. The security stack relies heavily on provider-native controls, satisfaction is high, but spending is low and a majority plan to change tooling within the year.
Across 101 enterprises, 57% report AI agents producing confident but wrong answers due to missing or inconsistent context in the past six months. Retrieval-augmented generation is the default context source, and provider-native retrieval (OpenAI 40%, Google 38%) has overtaken dedicated vector databases. However, a plurality (36%) intend to keep best-of-breed tools. Hybrid retrieval is expected to dominate by end of 2026, and 58% are building a governed semantic layer, but only 25% have it in production.
Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures.
A VentureBeat Pulse Research survey of 101 enterprises reveals that agent orchestration is consolidating on model-provider platforms, with Anthropic Claude leading at 40%. However, 71% admit that a quarter or fewer of their deployed 'agents' are true multi-step workflows, and only 10% have crossed the halfway mark. Enterprises plan hybrid control planes to avoid vendor lock-in, but real-time cost control remains immature.
Google announced a sweeping redesign of the search box at I/O 2025, transforming it into an AI-driven multimodal conversation interface. The company is merging AI Overviews and AI Mode, introducing generative UI, and launching information agents. Powered by Gemini 3.5 Flash, the new search experience shifts from keywords to natural language conversations, with significant implications for users, publishers, advertisers, and SEO.
San Francisco-based cloud platform Railway, which has amassed two million developers without marketing spend, announced a $100 million Series B round. With sub-second deployments, vertically integrated data centers, and per-second billing, it positions itself as a critical infrastructure startup in the AI era.
Anthropic's Claude Code pricing sparks developer backlash, while Block's open-source AI agent Goose offers similar functionality for free, running locally with privacy and offline support. This article analyzes Claude Code's rate limit controversy, Goose's features and setup, and the trade-offs in model quality, context window, speed, and tooling maturity.
Listen Labs, an AI-powered customer interview platform, raised $69M in Series B funding at a $500M valuation. The company gained attention with a creative billboard hiring challenge and has since grown annualized revenue 15x. Its platform conducts in-depth interviews via AI, replacing traditional surveys and focus groups, and is used by Microsoft, Sweetgreen, and others. The company plans to expand into synthetic customers and automated decision-making.
Salesforce has launched a fully rebuilt Slackbot powered by Anthropic's Claude, transforming it from a simple notification tool into an AI agent that searches enterprise data, drafts documents, and takes actions. The new Slackbot is available to Business+ and Enterprise+ customers at no extra cost. Internally, 80,000 Salesforce employees tested it, reporting high satisfaction and time savings. Pilot customers like Beast Industries report saving up to 90 minutes per day. Slackbot competes with Microsoft Copilot and Google Gemini, with Salesforce positioning it as a 'super agent' for the enterprise. The rollout begins today, with mobile availability by March.
Anthropic releases Cowork, a new AI agent that extends Claude Code's capabilities to non-technical users via a folder-based desktop interface. Built in about a week and a half, largely using Claude Code itself, Cowork is available as a research preview to Claude Max subscribers on macOS. It can read, edit, and create files, integrate with connectors and browser automation, and includes safety warnings about potential file deletion and prompt injection risks.
Nous Research, the open-source artificial intelligence startup backed by crypto venture firm Paradigm, released a new competitive programming model on Monday that it says matches or exceeds several larger proprietary systems — trained in just four days using 48 of Nvidia's latest B200 graphics processors.
Boris Cherny, creator of Claude Code at Anthropic, shared his personal terminal workflow on X, sparking a viral discussion. His approach includes running 5 Claude agents in parallel, using the Opus 4.5 model, maintaining a CLAUDE.md file for error lessons, and leveraging slash commands with subagents for automation. This workflow turns coding into a real-time strategy game, enabling a single developer to achieve the output of a small engineering team.