Overview
Anthropic shares their approach to building effective evaluations for AI agents. Unlike simple single-turn tests, agent evaluations must account for multi-turn interactions where mistakes can propagate and compound. The post outlines a structured framework with specific terminology and methods that teams can use to test AI agents more rigorously before production deployment.
Key Points
- Agent evaluations are fundamentally more complex than traditional AI testing because agents operate across multiple turns, use tools, and modify environment state - mistakes can cascade and create unexpected failure modes
- Teams should run multiple trials for each task since model outputs vary between runs - consistent measurement requires statistical rigor rather than single-shot testing
- Advanced models can sometimes ‘fail’ evaluations by finding creative solutions that exceed the test’s assumptions, like Opus 4.5 discovering policy loopholes - static evals may miss genuinely better approaches
- A comprehensive evaluation framework requires specific components: tasks with defined success criteria, graders with multiple assertions, complete transcripts of agent behavior, and infrastructure to run tests concurrently - structured evaluation prevents reactive debugging cycles
- The distinction between transcript (what the agent said) and outcome (actual environment state) is critical for accurate assessment - agents can claim success while failing to achieve the intended result