LangChain turns traces into repeatable agent evals

LangChain's eval-engineering skill maps an agent's harness and environment, can mine traces for realistic failure cases, then builds and audits one Harbor task at a time. Why it matters: Trace-derived cases only become useful regression tests when the harness, environment, and verifier are separable and inspectable. Otherwise a green score can be a leaked answer, an unrealistic sandbox, or a broken judge.

Try this: Take one recurring agent failure. Freeze its inputs and environment, write an independent verifier, then test one valid and one realistic wrong result before trusting the score.