Why AI-Generated Code Needs the Same Review Process as Human Code

We've spent decades developing software engineering practices: code review, security scanning, test coverage requirements, coding standards. These exist because they catch bugs, prevent vulnerabilities, and maintain quality.

Then AI coding tools arrived, and we threw it all out the window.

The Problem

When a human developer writes code, it goes through:

1. Code review - Another engineer examines the changes
2. Security scanning - Automated tools check for vulnerabilities
3. Test coverage - We verify tests exist for new code
4. Lint checking - Code meets team standards

When AI generates code, it typically goes:

1. Developer accepts suggestion
2. Commit

That's it. No review. No scanning. No coverage check.

Why This Matters

AI-generated code can have the same problems as human code:

• Security vulnerabilities - AI can generate SQL injection, XSS, hardcoded secrets
• Architectural issues - AI doesn't know your system's constraints
• Missing edge cases - AI handles the happy path, misses the edge cases
• Technical debt - AI optimizes for "works now" not "maintainable later"

If we require review for human code, why not AI code?

The Solution: Enforced Engineering Practices

I built BAZINGA to address this. It's a framework that enforces professional practices on AI development.

The Workflow

/bazinga.orchestrate implement user authentication

What happens:

1. PM analyzes requirements
   └── Breaks down into tasks, identifies concerns

2. Developer implements + writes tests
   └── Code AND tests, not just code

3. Security scan runs (mandatory)
   └── SQL injection, XSS, secrets, dependencies

4. Lint check runs (mandatory)
   └── Code style, complexity, best practices

5. Tech Lead reviews (independent)
   └── Architecture, security, quality, edge cases

6. Only approved code completes
   └── All gates must pass

Key Principle: Writers Don't Review Themselves

The Developer agent writes code. A separate Tech Lead agent reviews it.
This is the same separation of concerns we use in human teams. The person who wrote the code shouldn't be the only reviewer.

Mandatory Quality Gates

Every change gets:
| Gate | Tools | What It Catches |
|------|-------|-----------------|
| Security | bandit, npm audit, gosec, brakeman | Vulnerabilities, secrets, injection |
| Lint | ruff, eslint, golangci-lint, rubocop | Style, complexity, anti-patterns |
| Coverage | pytest-cov, jest, go test | Untested code paths |
| Review | Tech Lead agent | Architecture, edge cases |
These aren't optional. Can't skip them. Can't bypass them.

Structured Problem-Solving

When issues arise, BAZINGA applies formal frameworks:

• Root Cause Analysis - 5 Whys methodology, hypothesis matrices
• Architectural Decisions - Weighted decision matrices
• Security Triage - Severity assessment, exploit analysis
• Performance Investigation - Profiling, bottleneck analysis Not just "try to fix it"—structured analysis. ### Audit Trail Every decision is logged:
• What security issues were found
• What coverage was achieved
• What the Tech Lead reviewed
• What changes were requested Full traceability. Important for compliance. Important for learning. ## Example: What This Looks Like Request:

/bazinga.orchestrate implement password reset with email verification

Execution:

PM: "Analyzing request... Security-sensitive feature detected"
PM: "Assigning to Developer with security guidelines"

Developer: Implements password reset
Developer: Writes tests for reset flow
Developer: Tests edge cases (expired tokens, invalid emails)

Security Scan:
  ✓ No hardcoded secrets
  ✓ Token generation uses secure random
  ✓ Rate limiting present
  ⚠ Token expiration should be configurable (flagged)

Lint Check:
  ✓ Code style compliant
  ✓ Complexity within limits

Tech Lead Review:
  ✓ Token invalidation after use
  ✓ Audit logging present
  ✓ Error messages don't leak info
  Request: Add configurable token expiration

Developer: Adds configurable expiration
Security Scan: ✓ All clear
Tech Lead: ✓ Approved

PM: "All requirements met, all gates passed"
Complete.

Getting Started

# Install
uvx --from git+https://github.com/mehdic/bazinga.git bazinga init my-project
# Navigate
cd my-project
# Use
/bazinga.orchestrate implement your feature here

MIT licensed. Works with Claude Code.

The Philosophy

This isn't about slowing down AI development. It's about maintaining the same engineering standards we've established for good reasons.

AI-generated code should be:

• Reviewed by something other than the writer
• Scanned for security vulnerabilities
• Tested with measured coverage
• Validated against team standards

BAZINGA enforces this. Automatically. Every time.

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GitHub: github.com/mehdic/bazinga

What practices do you apply to AI-generated code? Let me know in the comments.