_agent-evaluation-user

Evaluating AI agent usability from a user perspective involves reviewing agent definitions to identify clarity of scope, output usefulness, question quality, and trust calibration across typical interactions, ultimately surfacing actionable findings to guide design improvements.

Trust calibration in AI agents is the balance of user confidence in the agent's outputs. You assess it by reviewing agent definitions for consistent guidance and expected user experience, ensuring the agent performs reliably within its defined scope.

Identifying scope legibility issues requires reviewing agent definitions to check if the operational boundaries and expected user experience are clear. It assesses whether users can easily understand what the agent can and cannot do.

Yes, evaluating agent definitions includes assessing the speed-depth balance. It checks whether the agent provides sufficiently detailed answers without excessive latency, ensuring output usefulness during typical user interactions.

The best way to review agent definitions for trust gaps is applying a user-facing evaluation skill. It analyzes question quality, scope legibility, and consistent guidance to surface usability issues and provide a brief report of concrete improvements.