LangChain Blog Answers Common Questions on Agent Evals for Agent Development

Agent evals are necessary to check if an agent behaves as expected, as agents differ from traditional software because outputs vary between runs, multiple answers can be correct, and agents choose their own steps. LangChain's platform, LangSmith, guides users to start by understanding agent traces, building a small dataset (10 to 20 examples) from traces, running a baseline experiment, and establishing an improvement loop by adding new failures found during testing or production. Evaluation types include Code, LLM-as-a-judge, and Human Review.