I'm a student building an open-source Android AI app on my own, and at some point I hit 400 unit tests with zero failures. That sounds great until you ask what those tests actually prove.

Quick context: Kryzz AI is an MIT-licensed Android app (Kotlin + Jetpack Compose) for chat, voice and agent tasks. It doesn't run models on the phone. You bring your own OpenRouter key and the models are hosted by whichever provider you pick. Chats, memory, files and keys stay on the device, and there's no analytics or telemetry.

What the tests are good at

An AI app has a lot of plain, boring code around the model, and that's exactly where tests pay off. Looking at the test folder, most files fall into a few groups:

Parsing what comes back. Streaming responses ( SseParserTest ), API responses, tool calls written as XML, and the agent's plan ( AgentPlanParserTest ). Models return slightly broken output all the time, so the parser has to survive it.

Rules the agent must follow.…