--- title: "AI-Generated Code Validation" description: "Validate AI-written UI and logic on real devices before merge, so agent speed never ships regressions or weak implementations." url: https://www.callstack.com/services/ai-generated-code-validation updated: 2026-11-09T15:58:52.130Z --- # AI-Generated Code Validation Validate AI-written UI and logic on real devices before merge, so agent speed never ships regressions or weak implementations. _[Agentic Delivery](/agentic-delivery) › [AI-Driven QA & Testing](/capabilities/ai-driven-qa-testing)_ **Merge AI code on evidence, not hope** Agents write more code than humans can hand-review. We validate AI-generated changes before merge - behavior, edge cases, and implementation quality - so faster output does not mean lower standards. ## AI-Driven QA & Testing - Industry runs on our tooling · 50M+ OSS downloads / month - 100+ Enterprise projects delivered - React Foundation Founding Member · 300+ commits to React Native · Contributing since 2016 - Expensify uses agent-device for bug evidence. Its usage story covers collecting evidence from mobile apps and profiling. - ~6x Faster prototyping · 60+ Components mapped with full coverage · Figma · Claude · Server · Tailwind · Tokens · React · Cursor · ESLint - 3.7× Lower task cost vs Opus 5.5 - Testing Library · Testing utilities in our open-source toolkit for checking React Native behavior. ## What you get: Every AI-written change, checked. - **PR validation runs** Each AI-authored change exercised on a real device, with evidence posted to the PR in minutes. - **Edge-case probing** Agents explore beyond the happy path the author - human or model - predicted. - **Implementation review** Patterns, structure, and architectural choices checked against your standards. - **Regression comparison** Behavior diffed against the previous build, so silent breakage is visible. - **Quality signals over time** Defect and rework rates on AI-assisted work tracked, so trust is earned with data. - **Human judgment where it counts** Clear escalation rules for what agents approve and what people review. ## Scope → Build → Scale: Design QA that keeps delivery moving. ### Scope (1 WEEK) We select a product workflow, define where agents act, and agree the controls and success measures. - Workflow and owners defined - Baseline and controls agreed - Token budgets confirmed ### Build (1-12 MONTHS) We connect context, agents, and verification to your tools, then run the workflow on your product backlog. - Running on your product backlog - Speed and quality measured - Business impact validated ### Scale (AS NEEDED) We document the workflow, transfer ownership, and extend it across teams and products based on results. - Your team owns the workflow - Expand based on results - Full knowledge handover ## What shipping at AI speed looks like. - [Building an Agentic SDLC for a Web Launch in Under One Month](/case-studies/building-an-agentic-sdlc-for-a-web-launch-in-under-one-month): 3x estimated delivery acceleration - [Engineering AI Loops for a Cross-Platform Design System](/case-studies/engineering-ai-loops-for-a-cross-platform-design-system): 60+ components mapped with full Code Connect coverage - [Migrating Questrade Edge Mobile to a Faster, More Scalable Architecture](/case-studies/migrating-questrade-edge-mobile-to-a-faster-more-scalable-architecture): 33% fewer re-renders across most features - [Enhancing Application's Performance for Expensify](/case-studies/enhancing-applications-performance-for-expensify): 5x faster message sending time **Keep the bar while raising the speed.** Validation that scales with agent output, so review stops being the bottleneck. [Add QA agent](/contact) > Their engineers proved to be a crucial part of our mobile app relaunch project. > > — Brian Aznar, Engineering Director, Major League Soccer We’ll connect you with the right expert within 24 hours. ## Let’s find the right path for your product. **Does this replace human code review?** No. It removes the mechanical burden - does it work, does it regress, does it follow standards - so human review concentrates on design and intent. **Which AI tools does it validate?** All of them. The validation targets the output, not the generator, so Claude Code, Cursor, Codex, and internal agents are covered equally. **How fast is the feedback?** Minutes per PR. Evidence - screenshots, recordings, findings - lands in the pull request while the author still has context. **How do you handle false positives?** Severity tuning and suppression rules per repo, reviewed regularly. The goal is a signal engineers trust, not a wall of warnings they ignore. **What metrics do we get?** Defect rates, rework, and review load on AI-assisted changes versus baseline - the numbers that tell you whether speed is costing quality. **Can it run in our infrastructure?** Yes. The validation stack runs on-premise, in your cloud, or hosted, matching your security requirements. ## Open source - [Agent Device](https://agent-device.dev/): CLI to control iOS and Android devices for AI agents - [React Native Testing Library](https://github.com/callstack/react-native-testing-library): Simple and complete React Native testing utilities that encourage good testing practices - [Reassure](https://github.com/callstack/reassure): Performance testing companion for React and React Native - [React Native Harness](https://github.com/callstackincubator/react-native-harness): Harness brings Jest-style tests to real native environments so you can test TurboModules like you would test your React Native app: with JavaScript. - [Rozenite](https://github.com/callstackincubator/rozenite): Plug-and-play panels for React Native DevTools. Install, enable, and start debugging in minutes.