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AI agent evaluation frameworks for production - Vercel

vercel.com · 2,494 words · saved by 1 readers

Learn how Vercel evaluates AI agents in production using ordered checks, binary metrics, trace grading, and CI gates to catch failures before they ship.

Evaluating an AI agent is a different problem from evaluating a single LLM call. A chat completion succeeds or it doesn't, full stop. An agent runs a multi-step workflow, chaining tool calls across retry state, and a wrong answer in step two doesn't stay contained to step two. It corrupts every step downstream that depended on it being right. When we built a durable AI code agent at Vercel, that shift changed the question we were asking. We stopped asking whether a given response looked good and started asking whether the workflow succeeded end to end. We see that same gap across most…

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