agent evaluations

Smarter AI agents shouldn't require costly real-world simulations to prove their worth

Testing AI agents in real-world environments is slow, expensive, and hard to reproduce. One researcher found a smarter path with AgentVQA, an offline benchmark that evaluates how well vision-language models understand visual information without costly simulations. It claims cleaner data and faster results. We think this approach could genuinely reduce the need for online evals. It's worth exploring if you're tired of the same old bottlenecks. For more on agent reliability, see our piece on when AI agents follow instructions but forget the procedure.