Building Effective AI Agent Evaluation: From E2E Tests to N-1 Evaluations

This article explores building efficient AI agent evaluation systems. The author stresses that while models constantly improve, evaluation remains crucial. It advocates starting with end-to-end (E2E) evaluations, defining success criteria and outputting simple yes/no results to quickly identify problems, refine prompts, and compare different model performances. Next, "N-1" evaluations, simulating previous user interactions, can directly pinpoint issues, but require maintaining updated "N-1" interactions. Checkpoints within prompts are also suggested to verify LLM adherence to desired conversation patterns. Finally, the author notes that external tools simplify setup, but custom evaluations tailored to the specific use case are still necessary.