AI Agent Evaluation Is the Foundation of Reliable Autonomy

Evaluating AI agents is crucial for ensuring reliable autonomy. A framework that assesses an agent's performance across various tasks, tools, and failure conditions is essential. Merely examining a model's output in isolation does not capture the full picture of an agent's behavior, as its decisions can impact subsequent actions. An evaluation framework provides the necessary evidence to determine whether a system can safely expand its autonomy.

The evaluation process should begin with well-defined criteria and scenarios that clearly outline the expected outcomes. For instance, in the case of a refund agent, success would be determined by verifying that the refund was processed correctly, the payment ledger reflects the change, and the agent's response aligns with the updated state.

The contract defining success should include explicit dimensions such as whether the task achieved an acceptable outcome, whether the agent adhered to its authority, and how the system handled failures or unexpected events.

Once the evaluation contract is established, the framework should employ a combination of deterministic checks and model-based grading. Deterministic checks can include ledger queries, authorization logs, and timers to measure the time taken for tasks. Model-based grading comes into play when interpreting the agent's responses or actions that require human judgment.

By comparing model-generated outputs with human evaluations on representative examples, the framework can identify discrepancies and refine the grading rubric accordingly.

A comprehensive evaluation framework should also account for resource usage, such as the number of tool calls and total execution time, to ensure the agent's performance is both effective and efficient. By continuously refining the evaluation scenarios and grading criteria based on new failures and feedback, the framework can help teams make informed decisions about expanding an agent's autonomy while maintaining reliability and safety.