How we build evals for Deep Agents
The blog emphasizes that evaluations are essential for refining agent behavior, shaping capabilities through thoughtful design, and measuring real-world performance. Evaluations are derived from feedback, benchmarks, and traces, with tools like Polly or Insights aiding in analysis. Metrics such as correctness, step ratio, and latency ratio help assess efficiency, while trace analysis identifies failure modes. The article outlines processes for running evaluations, grouping them by categories, and expanding the suite to include open-source LLMs.
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