I Built a Dual-Pool Adversarial Review System for AI Agents — And It Actually Works
Scored daily by a customisable AI persona to surface the most relevant engineering leadership news.
Novel AI agent review system with adversarial testing, highly relevant to AI orchestration and SDLC.
A dual-pool adversarial review system for AI agents uses fixed digital-twin personas (e.g., Patty McCord, Ed Catmull) and web-sourced random reviewers (e.g., Joel Spolsky) who cite specific principles—not generic roles—to produce grounded code feedback. The system's self-review on its own skill file discovered 16 issues, including a quote-retrofitting loophole and a broken file reference, all fixed in the live PR. Tested on a 18.7K-star PR (alirezarezvani/claude-skills), the random pool caught blind spots missed by fixed-pool reviewers, validating cross-orchestration of stable depth and fresh surprise coverage.