Demystifying evals for AI agents

What it does

Anthropic's field guide explains tasks, trials, graders, transcripts, outcomes, capability suites, regression suites, repeated trials, and evaluation maintenance. It recommends combining code, model, and human graders while reading raw traces.

• Tasks, trials, graders, transcripts, outcomes, eval harnesses, and agent harnesses
• Code-based, model-based, and human grading with outcome-oriented checks
• Capability versus regression suites, pass@k, pass^k, partial credit, and grader calibration

Where it fits

Product and engineering teams designing practical evaluations for multi-turn, tool-using agents.

Use it after defining the behavior that matters, collecting representative cases, and deciding how each case will be graded. A tool can execute and organize an eval, but it cannot decide which failures are costly for your users.

Limitations and cautions

• This is methodology, not executable software or a benchmark suite.
• Automated evals create false confidence when tasks do not match real use, and LLM judges still require human calibration.

How to evaluate the evaluator

Before using any score as a release gate, create a small evaluator-validation set that humans have labeled. Compare false passes, false failures, disagreement by failure type, run-to-run variance, latency, and cost. Keep deterministic checks for verifiable facts, calibrate model judges against domain experts, and review raw traces when aggregate scores move.