Agent Evaluation Readiness Checklist

Victor Moreira, a Deployed Engineer at LangChain, presents a comprehensive checklist for evaluating AI agents, emphasizing the importance of agent evaluation, which differs from traditional software testing. The guide outlines a systematic approach to building, running, and optimizing agent evaluations by starting with simple end-to-end evaluations to establish a baseline and gradually adding complexity based on evidence of failure. Key components include defining clear success criteria, separating capability evaluations from regression evaluations, identifying failure causes, and ensuring evaluation ownership by a domain expert. The process involves using tools like LangSmith for trace analysis, categorizing failures, and designing specialized graders for different evaluation dimensions. The article highlights the significance of offline, online, and ad-hoc evaluations, promoting successful evaluations into regression suites, and integrating them into CI/CD pipelines to maintain agent reliability. It stresses the need to iterate continuously by adapting evaluations based on production feedback and evolving test suites when pass rates plateau.