07/09/2026
If you’re trusting AI in its responses, you need to stop and read this.
Here's the problem nobody talks about with Claude Code: the model always agrees with itself. Ask Claude to review the code it just wrote and it'll pat itself on the back. That's fine for a copy tweak. It's dangerous when you're shipping auth, billing, or a database migration to a client's production system.
So I built a forcing function I call the Review Loop. Before any big change ships, it runs four phases:
1. Claude writes its case. Scope, architectural decisions, risks it already sees, alternatives it rejected — and the why behind each one. Not "trust me," an actual argument on paper.
2. Codex tears it apart. A second frontier model (GPT) reviews independently the code AND Claude's reasoning. It's prompted to be adversarial: find the auth hole, the data-loss path, the race condition, the migration that can't roll back. It never sees Claude's answers first.
3. Claude responds to every single finding. Accept, reject, or modify with written reasoning for each. Rejecting a finding requires a defensible reason on the record. "I disagree" doesn't count. If the reasoning starts to feel like rationalizing, the rule is to flip to accept.
4. I get one screen. Ship / fix-then-ship / don't-ship. What Codex caught, what's changing, what's staying and why.
After the Codex review, Claude now all the sudden realizes it made mistakes.
Every step lands on disk as an audit trail. Nothing gets softened, nothing gets skipped, and no code changes until the loop closes.
The insight isn't "use more AI." It's that a single model is a yes-man, and real review needs an adversary with no stake in the answer. Two models that disagree catch what one confident model ships straight past.
This is the kind of thing I build now, systems that make AI accountable before it touches something that matters.
If you're putting AI into real workflows and "the model said it's fine" is your last line of defense, you should consider running an adversarial model.