Enterprises that run more than a handful of AI agents in production face risk, cost and oversight burdens that grow exponentially with every agent they add, says Felix Van de Maele, founder and CEO of data governance company Collibra. In a conversation with VentureBeat founder and CEO Matt Marshall at VentureBeat's AI Week event in New York, Van de Maele traced most agent failures to the business context those agents receive — and said companies need deterministic controls on agent behavior before their agent count grows.

Falling token prices have led many enterprises to assume AI costs will fall with them. Agents are breaking that assumption.

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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Data platforms make decisions all day. An SQL query keeps the rows over a threshold, matches a keyword, and joins two tables on a shared ID. A pipeline sends a failed job down a retry path. Rules like these work when the answer sits in the data in a clean form.

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