LangChain publishes guide to eval-driven development for AI agents
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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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- LangChain BlogWhat Is Eval-Driven Development? How to Improve AI Agents
LangChain explains eval-driven development (EDD), a three-step process for building AI agents: define what good looks like, write the eval before you build, and compare results after each change. Because agents are non-deterministic, evals score runs across a dataset of examples and can use LLM-as-a-judge, with real agent runs in LangSmith as a source of test inputs.
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