Deep insights for enterprise AI, data, and security leaders
How enterprises coordinate and scale AI in production.
Twice as many companies use OpenAI's agent orchestration platform as Anthropic's, VentureBeat Intelligence found in its September survey on agent orchestration. OpenAI holds that lead even though Anthropic may lead OpenAI on other important indicators. For example, Ramp, the corporate card and spending company, reports that more businesses now pay for Anthropic than for OpenAI.
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If you sit in enterprise boardrooms today, the pressure weighing on CIOs comes down to two things: Velocity and ROI. Boards expect a mature agentic AI strategy and a definitive timeline for deploying autonomous systems that deliver measurable business impact.
The hardware and platforms underneath enterprise AI.
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GPUs running large language model inference can spend up to 60% of their compute recalculating context they already processed, says Alan McSeveney, field CTO at WEKA. Speaking with VentureBeat lead analyst Rob Strechay at the latest stop in the VentureBeat AI Impact Series, McSeveney said the waste goes back to how large language models hold conversation history in GPU memory, arguing that fixing it lets companies serve several times more users on hardware they already own. The efficiencies WEKA customers struggle with most show up in infrastructure, separate from the application metrics each business tunes for its own domain.
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Enterprise tech coverage through the lens of AI adoption.
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.
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.
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.
