{"id":80193,"date":"2026-07-28T11:49:53","date_gmt":"2026-07-28T06:19:53","guid":{"rendered":"https:\/\/tothenewco.pro\/blog\/?p=80193"},"modified":"2026-09-09T10:43:23","modified_gmt":"2026-09-09T05:13:23","slug":"ai-coding-agents-in-react-native-what-changed-after-we-used-them-in-production","status":"publish","type":"post","link":"https:\/\/tothenewco.pro\/blog\/ai-coding-agents-in-react-native-what-changed-after-we-used-them-in-production\/","title":{"rendered":"AI Coding Agents in React Native: What Changed After We Used Them in Production"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

A settings screen that normally took most of a day was built in under an hour using an AI coding agent. This made us explore tools like Cursor, GitHub Copilot, and Claude Code in a real React Native sprint.<\/p>\n

The goal wasn\u2019t just to see if AI could write code, but to determine whether it could reliably handle repetitive development work\u2014navigation, forms, and boilerplate\u2014without introducing more complex issues in architecture, async logic, or native iOS\/Android behavior.<\/p>\n

This post is that experience report: what worked, what didn’t, where the tooling actively cost us time, and what our review discipline looks like now as a result.<\/p>\n

Body
\nThe First Real Test
\n<\/strong>The first serious trial was a settings screen with form state, validation, loading states, and an API integration gated behind backend feature flags \u2014 not a toy example. The engineer wrote a prompt that specified field shapes, the expected payload contract, failure behavior per field, and which existing design-system components to reuse rather than reinvent.<\/p>\n

The first draft came back in minutes. It wasn’t production-ready out of the box \u2014 error handling defaulted to a generic toast instead of our app’s inline-error convention \u2014 but the skeleton was structurally sound, and correcting it took ten minutes instead of an afternoon spent typing boilerplate from scratch. That ratio \u2014 a fast, roughly-right first pass versus a slow, correct-from-scratch one \u2014 turned out to be the recurring pattern across the sprint, not the exception.<\/p>\n

Where AI Agents Actually Helped
\n<\/strong>Untangling Inherited Code
\nWe’d inherited a navigation stack from a previous contractor, with no design doc and half the original authors gone. Asking the agent to trace how a specific screen was reached \u2014 which stack, which params were passed at each hop, where a piece of state actually originated versus where it was just read \u2014 produced a working map instead of an afternoon of manual grep-and-guess. It wasn’t fully accurate on the first pass (it missed a conditional redirect buried in a useEffect), but a map that’s 85% right and needs verification beats no map, especially when the alternative is reconstructing five years of undocumented decisions by hand.<\/p>\n

Debugging a Stubborn Re-Render Issue
\nWe had a list screen re-rendering more than it should have, and the usual suspects \u2014 inline prop functions, missing useCallback \u2014 weren\u2019t the cause. After describing the symptom and sharing the component tree, the agent correctly pointed to a context provider higher up the tree that was recreating its value object on every render:<\/p>\n

\/\/ Before \u2014 new object on every render
\n<MyContext.Provider value={{ user, settings }}><\/p>\n

\/\/ After \u2014 memoized so children don\u2019t re-render unnecessarily
\nconst contextValue = useMemo(() => ({ user, settings }), [user, settings]);
\n<MyContext.Provider value={contextValue}><\/p>\n

We still verified the fix with the profiler ourselves, but the time to find the lead was a fraction of what it normally takes.
\nClosing the Test-Coverage Gap
\nTests are the first thing to get deprioritized under deadline pressure, which most teams will admit to. We started using the agent specifically to scaffold Jest and React Native Testing Library tests for components that were already finished. It defaults to happy-path coverage and misses edge cases unless explicitly prompted for them, but going from zero tests to a reasonable baseline \u2014 then refining manually \u2014 beat staring at an empty test file.<\/p>\n

Where It Fell Short<\/strong>
\nMissing Business Context<\/strong><\/span>
\nOne of our screens has a field that\u2019s optional for most users but required depending on subscription tier \u2014 a rule that came out of a support escalation, not anything visible in the code. The agent had no way to know that, and its first draft treated the field as universally optional. Not a flaw in the tool; just a reminder that business context lives in tickets and people\u2019s heads, not the codebase alone.<\/p>\n

Small Architectural Drift<\/span><\/strong>
\nA few times, generated code introduced a slightly different pattern than the rest of the file used \u2014 handling loading state with a boolean instead of the status enum (\u2018idle\u2019 | \u2018loading\u2019 | \u2018error\u2019 | \u2018success\u2019) the rest of the screen relied on. Functionally fine, but the kind of inconsistency that compounds if it isn\u2019t caught in review.<\/p>\n

The Temptation to Skip Review<\/span><\/strong>
\nThis was the most important lesson. Early on, an engineer accepted a generated API integration without reading it closely, and it shipped with a missing null check that only surfaced when a particular response came back without an optional field. Nothing catastrophic, but a clear signal that generated code needs the same scrutiny as any teammate\u2019s pull request.<\/p>\n

\u00a0Challenges We Ran Into<\/strong><\/span>
\nBuild issues:<\/span><\/strong> Suggested fixes often addressed symptoms rather than root causes, so issues reappeared in clean builds or CI.
\nDependency conflicts:<\/strong> Some upgrades fixed one issue but introduced new peer\/dependency problems, requiring additional investigation.
\nPlatform-specific gaps:<\/strong> AI performed well with JS\/TS but struggled with native iOS\/Android issues like CocoaPods, Gradle, and native crash debugging.
\nConfident wrong answers:<\/strong> Tools often provided fixes without clearly indicating uncertainty, making independent verification essential.
\nContext loss:<\/strong> Long sessions sometimes caused previously stated constraints or decisions to be forgotten.
\nKey takeaway:<\/strong> AI assistance is valuable, but \u201cthe agent says it\u2019s fixed\u201d should be treated as a hypothesis, not a verified state.<\/p>\n

Tool Comparison: Claude Code vs. Cursor vs. GitHub Copilot<\/span><\/strong>
\nThis wasn’t a formal benchmark \u2014 no controlled task set, no scoring rubric \u2014 it’s a working comparison from using all three inside the same React Native codebase over the same sprint, which is a fairer comparison than most vendor case studies offer but still an anecdotal one.<\/p>\n

How Our Workflow Looks Now<\/span><\/strong>
\nA few habits made the difference between useful output and wasted time:<\/p>\n

Write the requirement out properly first \u2014 fields, API behavior, edge cases \u2014 rather than a one-line prompt and hoping for the best.
\nTreat the first output as a draft, reviewed the same way as a teammate\u2019s PR, checked against existing patterns before merging.
\nLean on it heavily for repetitive, low-judgment work: test scaffolding, boilerplate forms, documentation, multi-file refactors.
\nKeep ownership unchanged \u2014 if something breaks in production, it\u2019s on the team, not the tool, which is what keeps the review step from becoming optional.<\/p>\n\n\n\n\n\n\n\n\n\n
Criteria<\/strong><\/span><\/td>\n Claude Code<\/strong><\/span><\/td>\n Cursor<\/strong><\/span><\/td>\n GitHub Copilot<\/strong><\/span><\/td>\n<\/tr>\n
Multi-file \/ whole-codebase reasoning<\/td>\nStrongest of the three \u2014 best at tracing logic across navigation stacks, contexts, and state ori<\/td>\nStrong, aided by its codebase indexing<\/td>\nWeakest here; optimized for local\/single-file context<\/td>\n<\/tr>\n
Following existing patterns (design system, status enums)<\/td>\nGood when the pattern is referenced explicitly in the prompt<\/td>\nGood, especially with .cursorrules defined<\/td>\nInconsistent without repeated, explicit prompting<\/td>\n<\/tr>\n
Debugging from a symptom description<\/td>\nStrongest in our test \u2014 correctly isolated the context-provider re-render issue from a description alone<\/td>\nSolid, benefits from live in-editor context<\/td>\nLimited \u2014 better suited to line-level completion than root-cause analysis<\/td>\n<\/tr>\n
Native build \/ platform-specific issues<\/td>\nWeak \u2014 shared blind spot across all three<\/td>\nWeak<\/td>\nWeak<\/td>\n<\/tr>\n
Test scaffolding<\/td>\nGood happy-path coverage; edge cases need explicit prompting<\/td>\nSimilar strength and same caveat<\/td>\nDecent for simple, low-branching components<\/td>\n<\/tr>\n
Editor integration<\/td>\nCLI-first, editor-agnostic<\/td>\nDeepest integration \u2014 it is the editor<\/td>\nDeepest integration with VS Code \/ JetBrains; most mature autocomplete of the three<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Our take, after nine years of watching tooling promises come and go: there’s no single winner here because the three tools aren’t competing for the same moment in the workflow.<\/p>\n

For investigative or cross-file work \u2014 tracing inherited navigation logic, root-causing a re-render bug, scoping a refactor \u2014 Claude Code was the strongest performer in our test, largely because it held broader context without needing the whole codebase open in an editor.
\nFor a tight, low-friction in-editor loop, Cursor was the most natural day-to-day tool \u2014 it minimizes context-switching in a way that matters more than raw capability during normal feature work.
\nFor fast inline autocomplete while writing routine code, GitHub Copilot remains the most mature and least disruptive to an existing VS Code or JetBrains setup, particularly for teams not ready to change their editor or workflow.
\nIn practice, most engineers on the team ended up using more than one tool rather than standardizing on a single winner \u2014 Cursor or Copilot for moment-to-moment typing, Claude Code when a task needed broader codebase context or a longer investigative back-and-forth. None of the three reliably solved native build or platform-specific issues, which stayed a manual debugging skill regardless of which agent was in the loop \u2014 a reminder that “AI-assisted” doesn’t yet mean “AI-covered” for the parts of mobile development closest to the metal.<\/p>\n

Conclusion<\/strong><\/p>\n

For our team, AI coding agents earned their place \u2014 not by replacing anyone\u2019s job or doubling our output across the board, but by absorbing the repetitive, low-judgment work that used to eat into everyone\u2019s day. That freed up real time for the parts of the job that still need a human: deciding what to build, understanding why users behave a certain way, and making the calls no prompt can make for you.<\/p>\n","protected":false},"excerpt":{"rendered":"

Introduction A settings screen that normally took most of a day was built in under an hour using an AI coding agent. This made us explore tools like Cursor, GitHub Copilot, and Claude Code in a real React Native sprint. The goal wasn\u2019t just to see if AI could write code, but to determine whether […]<\/p>\n","protected":false},"author":1681,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":0,"footnotes":""},"categories":[5881],"tags":[4782,5853],"class_list":["post-80193","post","type-post","status-publish","format-standard","hentry","category-react-native","tag-ai","tag-reactnative"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t