The LangChain team proposes a practical checklist for building an evaluation system, starting with manually reviewing real agent traces, emphasizing understanding failure patterns before automating.
LangChain introduces the 'Better-Harness' system, treating evaluations as 'training data' for agents, iteratively optimizing the engineering framework (harness) to improve agent performance, with a core focus on avoiding overfitting and achieving generalization.
The LangChain team shares their core philosophy for building AI agent evals: more tests don't mean better agents; the key is designing targeted, self-documenting evaluations that directly measure desired behaviors.