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Your CI pipeline worked fine when the app was young. Then the app grew. Features got split into modules. Teams formed around those modules. And somewhere along the way, what used to be a 4-minute build became a 25-minute one. Then 35. Now nobody pushes to main before lunch because the queue is already backed up. If this sounds familiar, you are not alone. According to Google’s 2024 Developer Survey, 83% of Android apps over 500,000 lines of code struggle with build performance.

Over-the-air (OTA) updates are one of the most powerful tools available to React Native teams. The ability to push changes directly to users’ devices without App Store review, without Google Play approval, without any action required from the user, meaningfully changes how fast a team can respond to bugs and iterate on their product. But OTA updates operate within clear boundaries. Misunderstanding those boundaries leads to two distinct problems.

We’ve been building an AI agent that can take a production bug, find the root cause in captured traffic, write a fix, and validate it before a human reviews it. We call it Agent Factory. Last week we ran it on ourselves, against a real bug in our own production service. The first thing we did was get the workflow wrong.

This is Part 2 of the AI Software Factory series. In Part 1, we established that the Agile methodology is buckling under the weight of “elastic code.” When AI agents can generate functionality in seconds, two-week sprints and manual task management become organizational bottlenecks. We introduced the concept of the AI Software Factory: a shift from managing human tasks to managing business intent through a “Funnel of Increasing Trust.” But a factory requires infrastructure.