Getting a minimal React Native UI running inside a native application usually takes around a week, and longer if the team is new to brownfield or React Native internals. The native teams usually run into: complicated setup, runtime and compiler errors, limited React Native context, and the time it takes to build a proper proof of concept.
In a brownfield setup, the team is not starting from zero. It is introducing React Native into an existing iOS or Android application, with existing build systems, native dependencies, release constraints, and engineering standards already in place. That changes the cost of even a small experiment.
Which is why we are here to unfold the migrations to React Native from weeks to days!
The rewrite is not the slow part
It is easy to talk about brownfield migration as if the main challenge is rewriting a native screen in React Native. The slower part is everything around the rewrite: getting the environment running correctly, extracting enough detail from the native screen, wiring the new React Native view back into the app, and checking that the behavior still holds once the new screen is in place.
This is one of those places where AI can drive autonomously on a single prompt.
Breaking down the AI-Assisted migration
At Callstack, we are building a framework that is capable of delivering successful migrations to react-native from any stack. Below is a visual that we displayed at KLJS showing how the AI Assisted Migration is orchestrated:
Foundation
The first component is the laying out the right foundations for the migration. This involves setting up the brownfield environment for both React Native and the Host App. Under the hood, this component is driven by the react-native-brownfield-migration skill. This allows the agent to scaffold the Android library module and iOS framework. It adds and updates the build configuration and setup files, links the xcframework and aar to the host native app and initializes the react-native instance, loads the JS Bundle and presents the react-native UI.
All of this is quite complicated if done manually. Let’s take a look at how quickly the AI Agent can add react-native to Mastodon iOS App:
Requirements
The second component is from the autonomous part of this framework and it works as the base of the migration. From here on, everything is orchestrated by the migration-planner. The agent has the flexibility to interact with the provided application or understand the provided screenshots of the target UI. When prompted, the agent leverages agent-device under the hood and gathers the respective context, layout, tokens, typography.
This information is then laid out in the form of tasks from a project management perspective. Each task contains enough context on what to implement and the next hand-off.
Development and testing
Once the migration-planner gathers the list of Tasks from the previous component, it then starts offloading the tasks to the developer agent. This agent has the required react, react-native and brownfield skills. Once a task is finished, it moves to testing phase.
In the Testing phase, the agent leverages agent-device and the reference UI to validate the implementation. If bugs are found, they are added into the qa-report which is then sent back This feedback loop continues until the tester gives a green light.
Where do we still need human intervention?
Agents can make mistakes. Brownfield environments leave too much at stake for teams to trust them without review. Dependency conflicts, build setup edge cases, and native integration details can all become more complex than a generalized workflow can handle.
That is especially true in custom setups. Simpler dependency conflicts can often be handled inside the workflow. Heavily customized environments are harder, and they stay harder.
So the promise here is narrower than autonomous migration. It is faster technical evaluation with engineering review still in the loop.
Bonus: AI-assisted greenfield migrations
As we shape up our migration framework to support both Brownfield and Greenfield initiatives, we’ve been putting our tooling through rigorous benchmarking. Our latest experiment highlights the massive efficiency gains on the horizon.
We tasked our framework with converting a screenshot of the Apple Clock app into a brand-new React Native app. Here is how it stacked up against human execution:
- Our Migration Framework: < 15 minutes | 85% accuracy
- Seasoned Developer: 47 minutes | 95% accuracy
While the developer maintained a slight edge in accuracy, the framework cut production time by nearly 70%. We are incredibly excited about these efficiency metrics and will be publishing more results and insights on our blog and social media platforms in the coming days.
Why this matters beyond the proof of concept
The main value of this kind of workflow shows up early, but it does not stop there.
Once teams can reduce the cost of setup, requirement gathering, implementation, and validation, they can move through brownfield evaluation with better signal and less wasted effort. That changes the quality of the technical discussion around migration. It also makes it easier to see where the real difficulty is likely to sit, whether that is build complexity, dependency alignment, shared data, or navigation between native and React Native surfaces.
That is useful for engineering teams first. It also gives decision-makers a clearer view of what the migration actually involves before the scope gets bigger than it needs to be.
