LangChain details LangSmith's full agent eval loop, from traces to simulated patient personas
LangChain · x · 2026-10-20
LangChain explains how LangSmith underpins its agent measurement and improvement loop:
- Every conversation lands as a trace, gets annotated by reviewers, and feeds the iteration loop.
- Datasets and experiments live in one place, letting teams run themed datasets against different model configs and compare results.
- Triage and improvement workflows read inputs and write scores back through LangSmith.
In a follow-up, LangChain notes agent evals run through the same graph: every eval, from single-reply fixtures to simulated multi-turn conversations with LLM patient personas, runs against the real agent — so what gets scored is exactly what a real patient would encounter.
Related event: Numan Builds Regulated AI Health Coach on LangGraph with Evaluation Loop(4 posts)→
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