Building State-Aware Agent Harnesses with LangSmith: From Ex-Post Evaluation to Live Steering
By
Ben LevinePatrick HendershottJune 29, 202613min
Summary
This guest post by Ben Levine and Patrick Hendershott from Candidly discusses how they built a state-aware agent harness using LangSmith. The article focuses on moving from ex-post evaluations (judging conversations after they end) to live steering of conversational AI assistants at the turn level. The authors explain their approach to building a turn-level view of interactions to optimize for resolution during conversations, rather than only measuring outcomes after the fact.
Source
Twitter / XBuilding State-Aware Agent Harnesses with LangSmith: From Ex-Post Evaluation to Live Steeringlangchain.comKey quotes
· 3 pulledMost conversational assistants are judged after the fact, by how the conversation ended.
To optimize for resolution during the conversation, the agent harness needs a turn-level view of where the interaction is and which response levers can move it forward.
Those labels define the objective, but they're observed only at the end, while the assistant acts turn by turn.
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