My AI Team Has Four Models and One Human in the Loop

My AI Team Has Four Models and One Human in the Loop Last week, GPT found a security bug in code that Claude wrote. Not a hypothetical. Not a contrived test. A real conversation-ownership vulnerability in a production app. If you started a chat, someone else could read your messages. Claude wrote the code. Claude reviewed the code. Claude missed it. GPT caught it in seconds. That moment changed how I think about AI-assisted development. The Single-Model Trap We all have a favorite model. Maybe it's Claude for reasoning, GPT for breadth, or whatever ships fastest. But here's the thing: every model has blind spots. And if you only use one model, you inherit all of its blind spots as your own. I've been building a workflow called TAT (Tiny AI Team) that treats AI models like an engineering team. Not one genius doing everything, but specialists collaborating, with me as the product owner making the final calls: Claude Opus orchestrates. Plans epics, breaks work into sprints, makes architec