LangChain publishes guide to eval-driven development for AI agents

LangChain published a tutorial describing eval-driven development (EDD), a three-step process for building AI agents: define what "good" looks like, write the eval before building, and compare results after each change. Because agents are non-deterministic, the guide says evals should score runs across a dataset of examples rather than rely on a single output, and can use an LLM as a judge. LangChain says real agent runs in its LangSmith platform can serve as a source of test inputs. The post presents EDD as a method teams can apply to their own agent development process; it does not report measured results from using it.

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