Agent Functional Testing: Test Cases, Coverage, and Limits

Agent functional testing is a requirements-based method for verifying that an AI agent follows a written capability specification defining what it must do, must never do, and must escalate. Unlike evaluations, which produce aggregate scores across many runs and help detect broad quality changes, functional tests provide pass-or-fail results for individual requirements and identify the specific rule that failed. Each test should verify both the agent’s real effect on connected systems and its customer-facing response, since an agent may perform an action correctly while describing it inaccurately, or vice versa. Effective coverage maps capabilities against input classes such as clear requests, vague requests, out-of-scope requests, limit boundaries, and adversarial instructions, exposing untested behavior that pass rates can conceal. The approach does not measure non-functional qualities including tone, latency, cost, and consistency, so critical cases should be repeated and supplemented with broader evaluations. TestMu AI is presented as a platform that can generate scenarios from capability specifications, execute them in parallel, and report per-scenario verdicts and confidence levels, though its results depend on the scope and quality of the supplied specification.

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