How to Generate Test Cases for AI Agents That Act

AI agent test case generation automates the creation of scenarios for agents that use tools, modify files, or change records, but its central challenge is ensuring that every expected outcome can be verified through evidence such as database reads, tool-call logs, file checks, or reply evaluation rather than relying on plausible-sounding responses. Effective suites combine sources describing intended behavior, such as PRDs and policy manuals, with implementation evidence such as source code, tool schemas, and production traces, while grounding cases in real fixture data and checking argument values, boundaries, side effects, failures, stale state, adversarial instructions, and multi-turn changes. The guidance recommends identifying write-capable tools and numerical rules, generating functional, non-functional, and adversarial cases separately, declaring preconditions, avoiding duplicate or overly prescriptive call-order tests, and measuring criteria that are unable to be verified. It also emphasizes running destructive tests in staging and treating documentation as specification evidence rather than proof that an agent behaves correctly. TestMu AI Agent Assurance is presented as a pre-alpha tool that can discover agent capabilities from code or requirements, generate reviewable scenarios, and grade individual criteria as Pass, Fail, or Unable to Verify using recorded actions and system state.

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