Agent Evaluation Readiness Checklist

Agent evaluation requires a different approach from traditional software testing. Rather than building a complex system from the start, you should begin with a simple end-to-end evaluation that checks whether the Agent completes its core tasks, then gradually increase complexity.

Before building evaluation infrastructure, manually reviewing 20 to 50 real Agent traces is essential. Using LangSmith's traces and annotation queue makes it possible to identify failure patterns in much greater depth than automated systems can.

Success criteria must be clear enough that anyone can agree on them. In addition, capability eval, which measures new features, must be separated from regression eval, which confirms that existing performance is maintained. The former sets targets for performance improvement, while the latter prevents regressions in existing functionality.

60 to 80% of the overall evaluation effort should be devoted to analyzing the causes of failures. It's important to collect failure cases, then classify them into a taxonomy such as prompt issues, tool design issues, and model limitations, in order to find and fix the root causes.

Finally, ownership of the evaluation process should be given to a single domain expert who will be responsible for maintaining the dataset and adjusting judging criteria, in order to maintain consistency.