New Agent Evaluation Checklist Outlines Five-Phase Framework for Building Reliable AI Systems
Summary
A new five-phase agent evaluation checklist is reshaping how teams build reliable AI systems, urging developers to manually review real agent traces, design specialized graders, and integrate continuous feedback loops into production pipelines.
Key Points
- A practical agent evaluation checklist is now available, covering five key phases: pre-eval preparation, evaluation level selection, dataset construction, grader design, and production readiness.
- Teams are urged to manually review 20-50 real agent traces before building any eval infrastructure, separate capability evals from regression evals, and use specialized graders per dimension rather than generic off-the-shelf metrics.
- Production readiness requires integrating regression evals into CI/CD pipelines, capturing live user feedback, versioning prompts and tool definitions, and continuously feeding production failures back into datasets to create an improvement flywheel.