Skip to content
🤖 Consolidated, AI-optimized BMAD docs: llms-full.txt. Fetch this plain text file for complete context.
BMAD Method BMAD Method BMAD Method

Adversarial Review

Force deeper analysis by requiring problems to be found.

What is Adversarial Review?

Section titled “What is Adversarial Review?”

A review technique where the reviewer must find issues. No “looks good” allowed. The reviewer adopts a cynical stance - assume problems exist and find them.

This isn’t about being negative. It’s about forcing genuine analysis instead of a cursory glance that rubber-stamps whatever was submitted.

The core rule: You must find issues. Zero findings triggers a halt - re-analyze or explain why.

Why It Works

Section titled “Why It Works”

Normal reviews suffer from confirmation bias. You skim the work, nothing jumps out, you approve it. The “find problems” mandate breaks this pattern:

  • Forces thoroughness - Can’t approve until you’ve looked hard enough to find issues
  • Catches missing things - “What’s not here?” becomes a natural question
  • Improves signal quality - Findings are specific and actionable, not vague concerns
  • Information asymmetry - Run reviews with fresh context (no access to original reasoning) so you evaluate the artifact, not the intent

Where It’s Used

Section titled “Where It’s Used”

Adversarial review appears throughout BMAD workflows - code review, implementation readiness checks, spec validation, and others. Sometimes it’s a required step, sometimes optional (like advanced elicitation or party mode). The pattern adapts to whatever artifact needs scrutiny.

Human Filtering Required

Section titled “Human Filtering Required”

Because the AI is instructed to find problems, it will find problems - even when they don’t exist. Expect false positives: nitpicks dressed as issues, misunderstandings of intent, or outright hallucinated concerns.

You decide what’s real. Review each finding, dismiss the noise, fix what matters.

Example

Section titled “Example”

Instead of:

“The authentication implementation looks reasonable. Approved.”

An adversarial review produces:

  1. HIGH - login.ts:47 - No rate limiting on failed attempts
  2. HIGH - Session token stored in localStorage (XSS vulnerable)
  3. MEDIUM - Password validation happens client-side only
  4. MEDIUM - No audit logging for failed login attempts
  5. LOW - Magic number 3600 should be SESSION_TIMEOUT_SECONDS

The first review might miss a security vulnerability. The second caught four.

Iteration and Diminishing Returns

Section titled “Iteration and Diminishing Returns”

After addressing findings, consider running it again. A second pass usually catches more. A third isn’t always useless either. But each pass takes time, and eventually you hit diminishing returns - just nitpicks and false findings.