How Do You Actually Evaluate an AI Agent?
Agent evals must run continuously using LLM-as-judge since non-deterministic outputs make exact-match testing impossible.
“The teams that really get this right treat eval sort of as a muscle. You build it early, you're running it constantly because I think the alternative is like finding out your agent tried recommending the wrong product three weeks ago from a customer complaint.”
LangChain practitioners argue that agent evaluation requires a fundamentally different approach than traditional software testing, because natural-language inputs and non-deterministic outputs make exact-match assertions useless. The recommended stack is LLM-as-judge scoring, real-world edge-case datasets, and eval pipelines that fire on every prompt tweak or model swap. The framing of evals as a continuous 'muscle' rather than a pre-ship gate reflects a maturing operational posture among teams shipping production agents.