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Part 2: AI Code Review Checkpoints – Where Human Intervention is Non-Negotiable

Home / Blog / Part 2: AI Code Review Checkpoints – Where Human Intervention is Non-Negotiable
  • 8 August 2025
  • appex_media
  • 28 Views

 

While AI-generated code accelerates development, these 5 critical checkpoints ensure it doesn’t compromise stability or security. Implement them to maintain velocity without sacrificing quality.

1. Pre-Commit: The First Line of Defense

What to vet:

  • High-risk areas (auth, payments, data processing)

  • Third-party dependencies (check for vulnerabilities via npm audit/snyk)

  • Complex logic (could this be a “magic” code bomb?)

Tools for automation:

bash
# Sample Git pre-commit hook
if grep -q "skip_validation=True" $FILE; then
  echo "🚨 Dangerous flag detected!" >&2
  exit 1
fi

Real example caught:
An AI added eval() for “dynamic flexibility” in a config parser – blocked pre-commit.


2. Pull Request: The Architecture Gate

Mandatory checks:

  • Service boundaries: Did the AI create hidden couplings?

  • DRY violations: Check for logic duplication

  • Contract changes: Verify API/Schema modifications

Team protocol:
“All AI-generated PRs require:

  1. Architecture diagram update

  2. Senior dev approval if touching core services”


3. Pre-Staging: The Integration Test Crucible

Critical tests to add:

gherkin
Scenario: AI-generated inventory service
  Given 1000 concurrent users
  When stock levels hit zero
  Then verify no negative quantities occur

Toolchain:

  • K6 for load testing

  • Pact for contract verification

  • Test containers for dependency mocking


4. Production Rollout: The Canary Savior

Deployment safeguards:

  1. Release to 2% traffic initially

  2. Monitor for:

    • Abnormal error rates (NewRelic/Datadog)

    • Performance degradation (Pyroscope)

  3. Automated rollback if:

    bash
  1. errors_per_minute > threshold || latency_ms > 500

5. Post-Mortem: The Feedback Loop

AI-Specific Retro Questions:

  1. “Did we need to generate this much code?”

  2. “What manual review steps failed?”

  3. “How can we improve our AI prompt guidelines?”

Example improvement:
After an AI caused a DB deadlock, teams now add:

python
# @ai-constraint: Must use row-level locking

The Control Checklist

For every AI-generated commit:

  1. Understandability audit (Can junior devs maintain this?)

  2. Security scan (Semgrep/SonarQube passed)

  3. Architecture review (No silent service couplings)

  4. Rollback plan (Tested in staging?)

Pro Tip:
Embed this in your CI/CD:

yaml
# .github/workflows/ai_safety_net.yml
- name: AI Code Review
  uses: your-org/ai-guardrails@v2
  with:
    risk_level: high
    require_human_review: true

Up Next: The Tool Deep Dive

In Part 3, we’ll configure Semgrep rules that automatically block dangerous AI patterns and implement AI-specific monitoring in Datadog.

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