Demystifying Evals for AI Agents: Anthropic's Engineering Guide
burny_tech · x · 2026-07-30
Anthropic's engineering team shared an in-depth guide on AI agent evaluations (evals).
- Necessity of Evals: The autonomy and multi-turn capabilities that make agents useful also make them difficult to evaluate. Good evals surface problems before they affect users, preventing reactive fix loops.
- Structure of an Eval: An eval provides a system with an input and applies grading logic to the output. As capabilities have advanced, evaluations have evolved from simple single-turn tests to complex multi-turn assessments.
- Complexity of Multi-turn Evals: In complex multi-turn scenarios (e.g., a coding agent building an MCP server), the agent uses tools, modifies state, and adapts to intermediate results. The evaluation design must match this systemic complexity.
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