LangChain argues that improving AI agents is fundamentally a data-mining problem that requires analyzing trace data at scale to surface behavioral signals. It proposes a workflow combining trace analysis, evaluation curation, and iterative experiments, drawing on open-source model fine-tuning and specialized agent systems to process large volumes of traces. The piece recommends alternating between harness engineering and fine-tuning as a sequential strategy for progressively improving agent performance.
