How to Make Agentic Adversarial Review Actually Adversarial

## Details

By now it’s standard advice, in the research and in practice: don’t let a model grade its own homework, bring in a second one to review it. It does find things. But it reviews inside the frame you handed it, and if it can detect what answer you’re hoping for, it tends to find its way there.

This is a walkthrough of a real Claude Code conversation where both of those bit me on my own work, and what I did about each. By that point Carmen Mardiros had a specific understanding of how sycophancy, autoregressive commitment, and recency bias behave, and used it to steer the conversation while it was running. That steering is most of what I want to show, so that other people can leverage the same mechanisms. It may change how you run reviews with the current generation of frontier models.

What you’ll leave with:
🎯 What to withhold from a reviewing agent so it challenges the premise rather than the detail
🎯 What to say to an agent that’s already agreed with you without triggering over-correction in the other direction
🎯 How to ask for evaluation, opinion and conclusions without leaking your implied bias to the model.

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