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# Human judgment in the agent improvement loop

**[LangChain](https://daily.dev/sources/langchain)** · 13 min read · 0 upvotes · 0 comments

## Summary

Building reliable AI agents requires capturing tacit organizational knowledge through a structured improvement loop. Using a financial trader copilot as a running example, the post explains how human judgment should inform workflow design, tool design, and agent context. The recommended approach centers on translating expert knowledge into automated evaluations rather than relying on manual review at scale. LangSmith features like annotation queues, online evaluators, and the Insights Agent help direct human attention to the most impactful cases. After deployment, real production data becomes the foundation for curated test suites and golden datasets that drive continuous agent improvement across iterations.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.langchain.com/blog/human-judgment-in-the-agent-improvement-loop>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain), [#context-engineering](https://daily.dev/tags/context-engineering), [#langsmith](https://daily.dev/tags/langsmith)

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