Orchestration

In computing, whenever a cheaper, more open alternative shows up, it’s often framed as a winner-takes-all fight to the finish. We saw it with Linux and Windows, and again with Android and iOS. In reality, both sides “won” those battles because they serve different markets, and seeing it as “either-or” was never the right lens to begin with.

GitHub Chief Product Officer Mario Rodriguez confirmed support for continuing work across devices, including mobile. That could matter to teams whose agents continue working after a developer closes a laptop or moves to another part of a project.

The broader proposition is to shorten the distance between discovering a problem and addressing it.

Researchers held five models fixed across three production harnesses. A vendor’s own harness bought no edge, and the only effect that beat the noise was a harness losing tasks.

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Presented by SAP

ScholarEvolve finds new ways to improve AI agents by turning academic research into code, boosting performance without retraining the underlying model.

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.