Demystifying evals for AI agents
- Source
- anthropic.com
- Author
- Anthropic Engineering
- Date
Context
An 's claim that it finished a task is only one part of the evidence. Anthropic's evaluation guide distinguishes the conversation record from the resulting state of the system: a booking assistant can announce success even when no reservation exists in the database. A useful test checks whether the intended change actually happened.
The guide also describes running multiple trials because results vary between runs. Since tools and the software controlling their use shape what the agent can accomplish, the test measures the model and that surrounding system together. A reliable assessment therefore needs observable success criteria and more than one convincing demonstration.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
“These same capabilities that make AI agents useful—autonomy, intelligence, and flexibility—also make them harder to evaluate.”
“Without them, it’s easy to get stuck in reactive loops—catching issues only in production, where fixing one failure creates others.”
“It “failed” the evaluation as written, but actually came up with a better solution for the user.”
“Agents use tools across many turns, modifying state in the environment and adapting as they go—which means mistakes can propagate and compound.”
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