Shipped a Full Production App in Cursor and Codex - Now What?
Shipping a production app with AI coding tools feels incredible. You go from idea to deployed product in days instead of months. But the celebration fades fast when you realize you now maintain thousands of lines of code you never actually wrote.
The Maintenance Problem Nobody Talks About
AI-generated code works. That's the easy part. The hard part is debugging it six weeks later when something breaks and you have no mental model of why the code is structured the way it is. You didn't make the architectural decisions - the model did. And it didn't leave notes about the tradeoffs it considered.
Every function looks plausible. The patterns seem reasonable. But when a subtle bug surfaces in production, you're reverse-engineering someone else's thinking - except that someone is a language model with no memory of its reasoning.
Building a Map After the Fact
The fix isn't to stop using AI coding tools. It's to build understanding alongside generation. After every major AI-generated module, spend time reading through it like a code review. Add comments explaining not what the code does, but why it exists.
Create a living architecture document that captures the decisions you accepted from the AI. When you let Cursor scaffold your auth system, write down what approach it chose and what alternatives existed. Future you will thank present you.
The Real Workflow
The developers shipping successfully with AI tools aren't the ones generating code fastest. They're the ones who treat AI output as a first draft that needs human understanding layered on top. Generation is cheap - comprehension is the bottleneck.
Maintain a changelog of AI-generated sections. Run the code with a debugger before shipping. Write tests that encode your understanding of the behavior, not just the happy path.
Fazm is an open source macOS AI agent. Open source on GitHub.