Automating Code Review with Multiple AI Models

AI code review isn’t new. But have you considered: how much better is a review by two independent AI models compared to one?

The answer: significantly. Just like human code review benefits from multiple reviewers, AI review benefits from multiple perspectives.

When one AI model reviews your code:

Perspective bias: The same model thinks in the same patterns, missing issues outside its comfort zone
Domain gaps: A backend-focused model might miss frontend XSS risks, and vice versa
Confirmation bias: The model may be too lenient on code patterns it commonly generates itself

Dual-Model Cross-Review with CCG

CCG Workflow’s review-audit strategy implements true independent cross-review:

> /ccg:go review the recent code changes

What happens:

Automatic git diff extraction
Codex reviews independently — focuses on backend logic, API security, data consistency
Gemini reviews independently — focuses on frontend interaction, XSS risks, accessibility
Claude merges both findings, deduplicates, and grades by severity

Example output:

## Review Report

### Critical
- [Codex] SQL injection risk in user query (line 42)
- [Gemini] Unsanitized innerHTML usage (line 158)

### Warning
- [Codex] Missing transaction rollback on error path
- [Gemini] Button missing aria-label for accessibility

### Info
- [Codex] Consider indexing the created_at column
- [Gemini] Component could be memoized for performance

The key: both models review independently, unaware of each other’s findings. This eliminates anchoring bias — one model’s conclusions don’t influence the other.

Real-World Coverage Comparison

For a typical full-stack PR (authentication module changes):

Issue Type	Claude Only	CCG Dual Review
SQL injection risk	✅ Found	✅ Found (Codex)
XSS vulnerability	❌ Missed	✅ Found (Gemini)
Race condition	✅ Found	✅ Found (Codex)
Accessibility issue	❌ Missed	✅ Found (Gemini)
Missing tx rollback	⚠️ Maybe	✅ Found (Codex)
CSS performance	❌ Missed	✅ Found (Gemini)

Dual-model coverage is substantially higher, especially for cross-domain issues.

Review Scenarios

Refactoring Review

> /ccg:go verify this refactor preserves behavioral equivalence

Uses the refactor-safely strategy:

One model analyzes pre-refactor behavior semantics
The other verifies post-refactor consistency
Ensures the refactor didn’t silently change behavior

Security-Focused Review

> /ccg:go security review the auth module

Automatically runs verify-security quality gate targeting:

Injection vulnerabilities (SQL, XSS, command injection)
Authentication/authorization flaws
Hardcoded secrets and sensitive data exposure
OWASP Top 10 coverage

PR Review Against Requirements

> /ccg:go review this PR against the requirements in SPEC.md

Combines requirements document with code diff for completeness verification.

Automated Quality Gates

Beyond manual review triggers, CCG automatically runs quality gates during complex task strategies:

verify-security — Scans for vulnerabilities
verify-quality — Checks complexity, naming conventions, function length
verify-change — Analyzes change impact, checks documentation sync

These gates run automatically in the verification phase of full-collaborate and guided-develop strategies. No manual trigger needed.

Getting Started

npx ccg-workflow

After installation, trigger reviews with natural language:

> /ccg:go review the changes on this branch
> /ccg:go security audit the payment module
> /ccg:go review last 3 commits for code quality

The engine selects the most appropriate review strategy automatically.

Learn more: Strategies | Quality Gates | GitHub