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Enterprise AI's real risk isn't autonomous agents. It's the complexity between them.
Presented by Gravitee
Rory Blundell, Gravitee
Partner Content
When agents act on their own, governance has to live in the data layer
Presented by EDB
Max Romanenko, EDB
Partner Content
Orchestration is the new challenge for CX in the age of AI agents
Presented by Tata Communications
VB Staff
VentureBeat names Rob Strechay as its first Lead Analyst, expanding its enterprise AI research push
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.
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Deep insights for enterprise AI, data, and security leaders
AI agents can be unpredictable. Your control layer shouldn't be.
In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context. Information can be lost or misinterpreted during a handoff, shared state can drift, and agents can get stuck in loops or deadlocks. When several agents rely on the same underlying model, they may even reinforce the same mistakes rather than catch them.
Shuhua Xu
H-JEPA teaches world models to plan at multiple levels of abstraction
Researchers at Yann LeCun’s Advanced Machine Intelligence (AMI) Labs and academic institutions have developed H-JEPA, a world-model architecture that learns to plan at different timescales using separate representations of the environment.
Ben Dickson
Why giving AI agents more compute isn't enough—and what Meta proposes instead
AI agents often struggle to assess their own progress and decide what to do next. They can waste compute resources following failed approaches, or they might overlook promising results or stop before completing a task. These problems become more costly as agents take on longer and more complex workflows.
Ben Dickson
Magnific One launches to give teams an image generator that avoids the 'AI look' and automatically upholds their brand kit
A system that centralizes those guidelines, identifies deviations and allows employees to correct the results could make AI generation more practical for campaigns requiring large numbers of creative assets.
Carl Franzen
The share of enterprises trusting AI agents to make production changes on their own has fallen from 75% to 56%, VB Intelligence finds
Those findings suggest unresolved gaps in the systems enterprises rely on to judge agent reliability.
Taryn Plumb
Google Cloud unveils persistent Gemini Agents for long-running tasks, and they get their own Gmail, Calendar, and Drive storage
While Google supplies its own Gemini models, the agent can already use Anthropic's Claude models, with support for additional proprietary and open-weight AI models planned.
Carl Franzen
Anthropic launches Claude Haiku 5.5 with 90% API price reduction, matching GPT-6 Luna
Anthropic calls Haiku its fastest model at standard speeds, while acknowledging that Opus in Fast Mode runs faster. It does not supply a tokens-per-second figure in the materials reviewed for this article.
Carl Franzen
You can now open Claude directly in Google Docs, Sheets and Slides (and vice versa, open and edit the files in Claude)
Anthropic’s move means Google Workspace customers no longer have to choose Gemini simply because it is the AI sitting closest to their files.
Carl Franzen
TwelveLabs debuts Pegasus 1.6 to improve robotics training data from first-person video
For an enterprise early in robotics, a useful starting point would be one bounded workflow, such as packing a particular product or assembling a specific part.
Carl Franzen
Partner Content
AI agents can’t fix infrastructure failures they can’t diagnose
Presented by Virtana
VB Staff
Atlassian deepens its OpenAI partnership with a spend commitment — but its platform stays firmly multi-model
The strategic signal is that Atlassian is deepening its OpenAI relationship — with real dollars behind it — while deliberately keeping its platform model-agnostic, with Anthropic and others very much in the mix.
Carl Franzen
Mistral debuts Large 4 ‘Le Chonk', a 1-trillion parameter text output model with high benchmarks planned for open weights release
Mistral executives said the Le Chonk name deliberately nods to the community that had been rooting for the company to build a massive frontier model.
Carl Franzen