LangChain publishes a practical guide on capturing tacit organizational knowledge — unwritten rules, conventions, domain expertise — into AI agent improvement loops.
LangChain published a framework detailing how to embed human judgment and tacit knowledge into AI agents throughout their development lifecycle. The guide uses a financial services SQL-generation agent as a concrete example, walking through how domain experts (traders, data scientists) contribute unwritten conventions to prompts, evaluators, and test suites. The core argument: most agent failures aren't LLM failures — they're knowledge-capture failures. The framework centers on a continuous improvement loop where production data, expert review, and evaluations compound over time.
The real bottleneck in agent reliability isn't model quality — it's missing tacit context: unwritten SQL conventions, domain-specific terminology, authoritative-vs-stale table knowledge. This guide gives a concrete architecture for structuring domain-expert interviews into prompt improvements, then baking those learnings into evaluators and regression test suites. The loop matters: production failures feed back into evals, not just one-off prompt patches.
Audit your current agent's system prompt this week — identify every assumption baked in that no domain expert explicitly signed off on, then schedule a 30-minute interview with the relevant SME to validate or replace each one.
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