LangChain's Better Harness: Hill-Climbing AI Agents to New Heights with Evals
LangChain just cracked the code on making AI agents smarter—without retraining models. Their Better Harness recipe uses evals to hill-climb performance, turning failures into rocket fuel.
⚡ Key Takeaways
- Evals act as 'training data' for agent harnesses, driving iterative improvements without model changes. 𝕏
- Source evals from hand-curation, production traces, and external sets; tag for efficiency and holdouts. 𝕏
- Holdout sets and human review prevent overfitting, ensuring production generalization. 𝕏
Worth sharing?
Get the best AI stories of the week in your inbox — no noise, no spam.
Originally reported by LangChain Blog
Related Stories
AI Tools
LangChain's Agent Middleware: The Custom AI Agent Builder You've Been Waiting For
AI Tools
LangSmith Fleet: LangChain's Bold Bet on Enterprise Agent Armies
AI Tools
LangChain's Agent Eval Checklist: Skip It, and Watch Your AI Crumble
AI Tools