AI BriefingLangChain Explains Eval-Driven Development for Improving AI Agents
·2026.11.10 02:43
Key point
The method uses a three-step process of defining pass/fail criteria, building datasets from real runs, and comparing baseline scores to detect regressions in agent behavior.
Details
- Eval-Driven Development (EDD) adapts Test-Driven Development for non-deterministic AI agents, focusing on consistent metrics and 'hill climbing' to reach evaluation goals.
- The process involves defining a Product Requirements Document (PRD) with specific pass/fail criteria, writing evaluations before building, and comparing changes against a baseline to identify regressions.
- Datasets should be built from real agent runs to ensure production relevance, while evaluators combine deterministic code checks for tool choice and LLM-as-a-judge for non-deterministic outputs.
- Post-deployment maintenance requires integrating evaluations into CI/CD pipelines, using online evaluators for live traffic sampling, and leveraging LangSmith Engine to convert production trace clusters into test cases.
- Common mistakes include defining vague success criteria, writing evaluations after fixes, relying solely on average scores, and trusting unvalidated LLM judges without human comparison.
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