With a vision-based agent, that part actually works well. It can look at the screen, understand intent, and perform actions across Android and iOS.
The bigger problem showed up around how tests are defined and maintained.
When test flows are kept outside the codebase (written manually or generated from PRDs), they quickly go out of sync with the app. Keeping them updated becomes a lot of effort, and they lose reliability over time.
I then tried generating tests directly from the codebase (via MCP). That improved sync, but introduced high token usage and slower generation.
The shift for me was realizing test generation shouldn’t be a one-off step. Tests need to live alongside the codebase so they stay in sync and have more context.
I kept the execution vision-based (no brittle selectors), but moved test generation closer to the repo.
I’ve open sourced the core pieces:
1. generate tests from codebase context 2. YAML-based test flows 3. Vision-based execution across Android and iOS
Repo: https://github.com/final-run/finalrun-agent Demo: https://youtu.be/rJCw3p0PHr4
In the Demo video, you’ll see the "post-development hand-off." An AI builds a feature in an IDE, and Finalrun immediately generates and executes a vision-based test for it verifying the feature developed by AI.