Story · Vivek Trivedy (LangChain)
Improving Agents is a Data Mining Problem (Vivek Trivedy (LangChain))
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LangChain points agents at other agents' traces to ask where an agent got dumber after compaction and where users got upset. On a legal benchmark with Harvey, an open model matched frontier-model trace judging at one to two orders of magnitude lower cost.
In plain words
- LangChain studied work records from artificial intelligence systems, looking for failures, frustrated users, and better ways to configure the systems.
- Those records show each action and outcome, including where performance worsened after earlier conversation was shortened.
- Other artificial intelligence systems can review many records and compare what different systems might have done.
- In Harvey's legal test, an open system judged records as well as a leading system at much lower cost.
- This matters for teams improving these systems, because real work records can reveal faster configuration changes before further training.
Appeared in
- Mind viruses spread between LLM agents, and a one-line warning nearly stops them
Aug 13, 2026 · in the sections
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