Demystifying evals for AI agents

Anthropic released a guide on how to evaluate AI agents, showing why multi-turn, tool-using behavior needs a different approach than simple single-turn tests.

The guide defines key terms such as task, trial, grader, transcript, outcome, harness, and suite to make evaluation design more consistent.

Because agents can fail across many steps and tools, stronger evals help teams catch issues earlier and compare models more reliably.

The guidance aims to help teams ship autonomous agents with lower user risk, using examples from Claude Code, Agent SDK, MCP servers, Opus 4.5, and τ2-bench.