How we build evals for Deep Agents
📰 LangChain Blog
Building effective evals for Deep Agents involves directly measuring agent behavior and creating targeted experiments to improve accuracy and reliability
Action Steps
- Source relevant data to inform eval design
- Create metrics that directly measure desired agent behavior
- Run well-scoped and targeted experiments to test agent performance
- Refine and update evals over time to ensure ongoing improvement
Who Needs to Know This
AI engineers and ML researchers benefit from this approach as it enables them to develop more accurate and reliable agents, while product managers can use these evals to inform product decisions
Key Insight
💡 Effective evals should directly measure agent behavior and inform targeted experiments for improvement
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🤖 Build better agents with evals that measure what matters!
Key Takeaways
Building effective evals for Deep Agents involves directly measuring agent behavior and creating targeted experiments to improve accuracy and reliability
Full Article
TLDR: The best agent evals directly measure an agent behavior we care about. Here's how we source data, create metrics, and run well-scoped, targeted experiments over time to make agents more accurate and reliable. Evals shape agent behavior We’ve been curating evaluations to measure and
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