LangChain lays out three-step eval-driven development for improving AI agents
LangChain · x · 2026-11-10
Divya Santhanam of the LangChain Academy team walks through eval-driven development (EDD): building an agent while measuring against a consistent set of metrics and hill climbing toward an eval.
- Step one is a short spec of what good looks like, including what the agent shouldn't do and what has to keep working.
- As you iterate, judge each change by comparing experiments, since a fix for one case may break another.
- Keep evals running in CI and in production, and turn failures into new dataset examples.
The post notes that hand-testing works early on but makes it increasingly hard to tell whether the latest change helped or hurt, which is what EDD addresses.
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