Conclusion

In this lesson, you took the three audits produced by the previous module — goals and non-goals, corner cases and acceptance criteria, Dynamic Type, and VoiceOver — and consolidated them into a single, defined list of proposed unit tests. Each proposed test was tied back to a specific finding, giving you a checkpoint to review, adjust, or reject it before any test code was written. Only once that list was approved did the agent implement the tests themselves.

The key takeaway is that a test only earns its place in the suite once you know what it’s guarding against. Writing tests from a reasoned, human-approved list produces a suite that maps directly onto real findings from real audits — not an arbitrary coverage number. An open-ended “write more tests” prompt can’t give you that traceability.

You now have a set of unit tests guarding the bugs and gaps this module surfaced. The next lesson turns to a different kind of testing entirely: using an AI agent to drive the running app in the simulator directly, executing test steps itself the way a person would.