Learn how to build targeted evaluation systems that directly shape agent behavior by measuring real-world capabilities like tool use, retrieval, and multi-step reasoning. This approach emphasizes curating evals from production traces, dogfooding feedback, and adapted benchmarks, rather than relying on large generic test suites. The system uses categorized evals (e.g., tool_use, memory, retrieval) and metrics such as correctness, step ratio, tool call ratio, latency ratio, and solve rate to assess both accuracy and efficiency. By analyzing traces and defining ideal execution trajectories, teams can iteratively improve agent performance, reduce cost, and prevent regressions while maintaining alignment with production needs.
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Better-Harness: Using Evals to Iteratively Improve Agent Harnesses
Use evaluation-driven feedback loops to iteratively improve agent harnesses and achieve better generalization in production. Better-Harness treats evals as training data for agents, where each test case provides a learning signal to optimize prompts, tools, and workflows. The system combines curated eval sourcing (hand-written cases, production traces, external datasets), structured tagging for behavioral coverage, and holdout sets to prevent overfitting. It introduces a compound system approach—data sourcing, experiment design, optimization, and human review—to continuously refine agent performance. Key practices include mining production traces for failures, using tagged eval subsets for cost-efficient testing, and pairing automated improvements with human validation to avoid reward hacking and ensure real-world reliability.