The point isn’t to replace judgment. It’s to catch more mistakes before the work reaches you.
Think about sending an important proposal through a second reviewer before it goes to the client. The first person writes it; the second reads it with fresh eyes and says “this number doesn’t tie” or “this claim needs a source.” You still own the final send. The second reviewer just improves the draft before it lands on your desk. That’s the shape: Claude Code writes something, Codex reviews it and may find a bug or a weak assumption, then Claude Code fixes it and sends it back through review again.
How it shows up
We use this as a practical quality-control pattern: one model builds, another reviews, and the loop continues until review stops finding meaningful issues. In code work it often happens before a pull request; in writing work, one model drafts and another checks for clarity, unsupported claims, or tone drift. The value comes from difference, since models have different tendencies and one may catch what the other misses.
Why you care
Two AIs agreeing doesn’t make the answer true. They can share the same blind spot, especially reading the same bad context, so the human stays accountable. If an AI drafts a client memo and another reviews it, that doesn’t make it safe to send, it just means it’s less raw when you read it. You still check the facts and whether the work fits the client and the risk. Cross-model review is one kind of guardrail, not the only one.