i’ve been trying to get rid of adversarial reviews for quite a while now, but unfortunately every time i stopped it, i would find myself simply delaying the discovery of real problems, and the later i find them the more they cost as a data point, i had measured how many real problems my adversarial reviewers actually caught, and it’s a lot.. that said, a good adversarial review setup is necessary for the ROI to be justifiable. i think a few things are very important: 1. original user intent must be explicitly collected (usually can be found in session transcripts where user inputs are) and passed to the reviewers 2. there needs to be a human in the loop for ambiguous decisions. any scope change / expansion needs to escalated to the human user for decision 3. reviewers need to treat complexity like a disease and actively point out simplification opportunities 4. reviewers need to challenge the lack of empirical evidence and hold the implementer accountable to prove their change