LangChain explains how it builds targeted evals for Deep Agents
sharpeye_wnl · x · 2026-10-26
LangChain published a detailed blog on building evals for Deep Agents, its open-source model-agnostic agent harness powering Fleet and Open SWE.
Key points
- Evals shape agent behavior: each failing eval prompts tweaks to prompts or tool descriptions, applying pressure on the system over time
- More evals ≠ better agents; blindly stacking thousands of tests creates an illusion of improvement. Build targeted evals reflecting production behaviors instead
- Workflow: catalog behaviors that matter (multi-file retrieval, composing 5+ tool calls), source data from dogfooding and trace reading, group evals by what they test, define metrics, and run well-scoped experiments continuously
Related event: LangChain Explains How It Builds Evals for Deep Agents(2 posts)→
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