From Intent Classification to Open-Ended Action Spaces: Why Mobile Testing Needed a New Paradigm

From Intent Classification to Open-Ended Action Spaces: Why Mobile Testing Needed a New Paradigm I'm the creator of Drengr, an MCP server that gives AI agents eyes and hands on mobile devices. I started this blog to share the engineering behind it. No pretending to be a neutral observer writing a think piece — I built this, and I'm here to talk about it. Google recently shipped AI Edge Gallery — an on-device AI sandbox app with a feature called "Mobile Actions" that lets you control your phone with natural language. Say "turn on the flashlight," and a 270M parameter model called FunctionGemma figures out the intent, extracts the parameters, and dispatches the right function call. It runs entirely offline. It clocks 1,916 tokens/sec prefill on a Pixel 7 Pro. And it's impressive. But it also reveals a ceiling. The Closed-World Assumption FunctionGemma is, at its core, a tiny NLU engine performing intent classification and slot filling. You speak. It classifies your sentence into one of a