How to Choose the Right Eval for an AI Agent

Short summary

AI agents comprise multiple components—router, tools, skills, memory, retrieval—that each fail differently and require targeted evaluation. Match evaluation type to problem: code-based evals for deterministic checks, LLM judges for qualitative assessment, and human review for high-stakes or domain-specific decisions. Decompose testing by component to understand what failed and where, rather than treating agents as black boxes.

• •Break agents into components (router, tools, skills, memory) for targeted evaluation rather than black-box testing
• •Match eval type to problem: code-based for deterministic checks, LLM-judge for qualitative, human for high-stakes
• •Component-level evals pinpoint root causes and enable targeted fixes

Generated with AI, which can make mistakes.