Mike Cohn’s test pyramid has ruled Android testing since 2009: lots of unit tests, fewer integration tests, very few UI tests. The pyramid was right for its era — integration tests were slow, brittle, and imperative. But something changed in 2026: AI can write Android code, and an AI agent can only iterate as well as its oracle. Imperative Espresso tests don’t give an agent enough semantic ground to reason from. Unit tests can’t verify whether the integrated app actually works. And the classic pyramid, read literally, leaves an agent without a trustworthy feedback loop. This talk argues for inverting the pyramid on Android, and shows how we did it in production. We rebuilt our integration testing foundation in four layers: a standardized robot pattern, GraphQL fixtures for deterministic data, a declarative DSL where scenarios describe intent rather than steps, and AI-generatable scenarios on top. Because scenarios describe what a test is for instead of how it steps through UI, Claude can both generate them reliably and read them back as a check on its own work. Paired with a fast test runner, this turns the integration suite into what we call a fast agentic source of truth — the AI writes a scenario, runs it against the live app in seconds, reads the result, and iterates. The numbers land: one of our feature modules now sits at 91.5% integration coverage, high enough that its unit tests are largely redundant. 37 scenarios run in production across three feature modules. The pattern is our team’s recommended testing approach. You’ll leave with the patterns, the tradeoffs, and enough concrete detail to try the inversion on your own codebase — no matter what testing framework you start from.