AI agents can now write application code, operate simulators and inspect screens. However, their productivity depends heavily on how quickly they can test a change and understand the result.
A recent GeekyAnts analysis of AI-assisted mobile development explains this through React Native. JavaScript changes can appear through Fast Refresh within seconds, while changes involving native dependencies or platform configuration may require a much slower rebuild. These delays become significant when an AI agent needs several attempts to complete a task.
Application design also affects how reliably an agent can interact with the interface. Clear accessibility labels, stable test identifiers and predictable launch states help agents locate controls without depending on fragile screen coordinates.
Simulators are useful for rapid iteration, but physical-device testing remains important. Real devices can reveal performance, hardware, haptic and thermal issues that simulations may miss.
The broader takeaway is that organizations adopting AI coding tools should measure more than the amount of code generated. Build time, failed interactions, testing reliability and real-device performance are better indicators of whether AI is actually improving software delivery.
How is your development team measuring the productivity of AI coding assistants?