Why AI-Generated Code Needs a Quality Layer

Why AI-Generated Code Needs a Quality Layer When we started RLabs, we believed in a simple promise: let AI agents write code faster. But we quickly discovered a harder truth—speed without structure creates disasters. I've watched Claude and GPT produce dozens of lines of plausible-looking code that falls apart the moment you try to run it. Hallucinated imports. Inconsistent architecture. Missing error handling. Functions that assume globals exist. Async/await patterns that deadlock. It's not the models' fault. They're trained to continue patterns, not to architect systems. The first instinct was predictable: "Let's just prompt harder." Ask the agent to use a specific pattern. Add examples to the context. Tell it to validate its own output. Some of this helps, but it doesn't scale. Every project needs slightly different rules. Every team has different conventions. And most critically—checking your own work is not the same as building with constraint. We built AgentGuard to solve this. T