Demystifying Evals for AI Agents
Source: https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents Author: Anthropic Engineering Date: 2026-01-26
Summary
Anthropic’s framework for evaluating AI agents. Distinguishes between different eval types and when each is appropriate. Argues that good evals are the hardest part of agent development — not the model.
Key Claims
- Agent evals differ from model evals: you’re testing a system (model + tools + memory + prompts), not just a model.
- Three eval types: (1) unit evals — single tool call correctness; (2) integration evals — multi-step task completion; (3) end-to-end evals — full task success including side effects.
- Key insight: most teams over-index on (1) and skip (3), which is where agents actually fail in production.
- Ground truth is hard: agent tasks often have multiple valid solutions — eval harness must handle this.
- The “eval loop” should run continuously, not just at release: agents degrade as the world changes.
- Recommendation: start with narrow tasks where success is binary; generalize evals as system matures.
Entities
Concepts
- Coding Agents — primary eval target
- Reward Hacking — poor evals create reward hacking surface
- Autonomous Research — eval infrastructure enables continuous improvement loops