Source record

Published by LangChain Blog (T1.5 source). The original is at https://www.langchain.com/blog/improving-agents-is-a-data-mining-problem.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

LangChain describes treating agent improvement as a data mining problem: mining execution traces to find failures, fine-tuning smaller open models as judges to process trace data at scale, and using evals to hill-climb agent performance through continual learning loops.

Frames agent improvement as an iterative data pipeline rather than prompt tweaking, with concrete hooks for teams already running agents in production.

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