How we build evals for Deep Agents

1. 1Source relevant data to inform eval design
2. 2Create metrics that directly measure desired agent behavior
3. 3Run well-scoped and targeted experiments to test agent performance
4. 4Refine and update evals over time to ensure ongoing improvement

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

💡 Effective evals should directly measure agent behavior and inform targeted experiments for improvement

🤖 Build better agents with evals that measure what matters!