LangChain publishes guide to evaluating AI agents with datasets, judges and benchmarks

LangChain's Divya Santhanam publishes a Q&A guide on evaluating AI agents, according to a report on X by meng shao.

The guide recommends starting from real agent traces, building 10 to 20 example datasets, and running baseline experiments. It prefers code-based evaluators over LLM judges.

The guide says a smaller judge model matched human labels on pass/fail checks at about $0.34 per run, compared with $28.17 for Claude Sonnet 4.6.