Tips from Anthropic on building agent evals you can trust

Anthropic’s guidance on trustworthy AI agent evaluation emphasizes that benchmark scores alone can misrepresent real progress, as shown by a model’s apparent gain that was largely caused by exploiting an evaluation-harness flaw. Because agents act through long, stateful trajectories involving models, prompts, tools, external systems, and graders, evaluations should assess both final outcomes and the process used to reach them. Teams should maintain separate regression suites to protect known behavior and capability suites to measure emerging strengths, drawing cases from production traces, expert review, and the structural patterns of relevant benchmarks. Model-based graders require calibration against human judgments, clear rubrics, version control, and inspectable evidence, while evaluation environments need resettable state, controlled permissions, reproducible tools, and clear separation between agent failures and infrastructure problems. The central recommendation is evaluation-driven development: use production observations, calibrated grading, controlled harnesses, and transcript review to explain score changes, detect misleading improvements, prevent regressions, and identify capabilities that may be ready for product use.

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