Last week I shipped a cross-platform app and needed to test it on Flutter, React Native, iOS, Android, Electron, Tauri, and web. Writing separate test suites for each platform? No thanks. Instead, I used an AI agent that could see my app and interact with it. Here is what the workflow looked like: I used flutter-skill, an open-source MCP server that gives AI agents eyes and hands inside running apps. It connects to your app via a lightweight bridge and exposes 253 tools the AI can use. npm install -g flutter-skill flutter-skill init ./my-app flutter-skill launch ./my-app

Instead of writing test code, I just described what to test: Test the login flow - enter test@example.com and password123, tap Login, verify the Dashboard appears The AI agent automatically: Takes a screenshot to see the current state Discovers all interactive elements with semantic refs Taps, types, scrolls - just like a human Verifies the expected outcome Screenshots each step for evidence Across 8 platforms, the AI agent completed 562 out of 567 test scenarios (99.1% pass rate). The failures were all legitimate bugs it discovered. What surprised me most: Zero test code written - everything was natural language Cross-platform for free - same test descriptions worked on iOS, Android, web, desktop Found real bugs - the AI explored edge cases I would not have thought of Snapshot is 99% more token-efficient than screenshots - the accessibility tree gives the AI structured data instead of pixels Use AI testing when: You need to test across multiple platforms quickly You want to explore edge cases without writing explicit tests Your team does not have dedicated SDET resources You need fast smoke tests during development Stick with traditional automation when: You need deterministic, repeatable CI/CD tests Performance benchmarking Testing specific race conditions flutter-skill is open source and free: github.com/ai-dashboad/flutter-skill Works with Claude, GPT, Gemini, Cursor, Windsurf, and any MCP-comp