Better Harness: A Recipe for Harness Hill-Climbing with Evals
📰 LangChain Blog
Building better agents requires building better harnesses with a strong learning signal to hill-climb on, achieved through evals
Action Steps
- Identify key components of a harness that impact agent performance
- Design an evaluation framework to assess harness quality
- Use evals as a learning signal to iteratively improve the harness
- Implement hill-climbing algorithms to optimize harness development
Who Needs to Know This
Product managers and AI engineers on a team can benefit from this approach as it enables the development of more effective agents, and evals provide a clear direction for improvement
Key Insight
💡 Evals provide a strong learning signal for autonomously building better harnesses, enabling more effective agent development
Share This
🚀 Build better agents with better harnesses, using evals as a learning signal to hill-climb to success!
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