AI Agent Code Review: How to Ensure Safety Before Merge

Code review is the last human checkpoint before AI agent policy changes reach production — it must surface exactly what permissions are changing and whether safety tests pass. SafeClaw by Authensor generates human-readable policy diffs, highlights permission escalations, and runs simulation tests as part of your pull request workflow. Reviewers see precisely which agent capabilities are being added, removed, or modified, making it impossible for dangerous permission changes to slip through review unnoticed.

Quick Start

npx @authensor/safeclaw

Scaffolds a .safeclaw/ directory. Then configure your PR workflow as described below.

Step 1: Generate Policy Diffs for Pull Requests

Add a CI step that generates a policy diff and posts it as a PR comment:

# .github/workflows/policy-review.yml
name: Policy Review
on: [pull_request]

jobs:
  policy-diff:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0  # Full history for diff

- name: Generate Policy Diff
        run: |
          npx @authensor/safeclaw diff \
            --base origin/${{ github.base_ref }} \
            --format markdown \
            --show-escalations \
            > policy-review.md

- name: Post Review Comment
        uses: actions/github-script@v7
        with:
          script: |
            const fs = require('fs');
            const review = fs.readFileSync('policy-review.md', 'utf8');
            if (review.trim()) {
              github.rest.issues.createComment({
                issue_number: context.issue.number,
                owner: context.repo.owner,
                repo: context.repo.repo,
                body: review
              });
            }

Step 2: What Reviewers See

The policy diff provides a structured summary that reviewers can evaluate quickly:

## SafeClaw Policy Review

Permission Changes
| Rule | Action | Previous Effect | New Effect | Risk |
|------|--------|-----------------|------------|------|
| allow-src-writes | file.write | allow (src/) | allow (/*.ts) | ⚠ ESCALATION |
| block-env-access | file.read | deny (.env*) | — (REMOVED) | 🔴 CRITICAL |
| new-api-access | api.call | — (NEW) | allow (*.api.com) | ⚠ NEW PERMISSION |

Escalation Details

allow-src-writes: Path pattern expanded from src/ to /*.ts.
  Agent can now write TypeScript files anywhere in the project.

block-env-access: REMOVED. Agent will now be able to read .env files
  (falls through to default deny only if no other rule matches first).

Simulation Test Results
✓ 13/15 tests passed
✗ 2 tests failed:
  - "Agent cannot write outside src" — EXPECTED deny, GOT allow
  - "Agent cannot read .env" — EXPECTED deny, GOT allow

Step 3: Make Safety Tests a Required Check

In your GitHub repository settings, mark the safety test job as a required status check:

  safety-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Safety Tests
        run: npx @authensor/safeclaw test --strict
        # --strict fails on any warning, not just errors

With this configuration, PRs cannot be merged unless all simulation tests pass. If a developer broadens agent permissions, the corresponding tests must be updated too — and the reviewer sees both changes in the same PR.

Step 4: CODEOWNERS for Policy Files

Require security team review for any policy change:

# .github/CODEOWNERS
.safeclaw/   @security-team

This ensures that changes to .safeclaw/policies/, .safeclaw/tests/, and .safeclaw/config.yaml require approval from the security team before merge.

Step 5: Review Checklist

Include a policy review checklist in your PR template:

## AI Agent Safety Review

[ ] No permission escalations without documented justification
[ ] All new allow rules have corresponding deny rules as fallbacks
[ ] Simulation tests cover the new/modified rules
[ ] No rules removed without replacement
[ ] Audit trail configuration unchanged

Why SafeClaw

• 446 tests — the diff and validation tooling is as reliable as the policy engine itself
• Deny-by-default — new agent capabilities are blocked until policy changes are reviewed and merged
• Sub-millisecond evaluation — simulation tests run fast enough for interactive PR workflows
• Hash-chained audit trail — every policy change is logged with tamper-proof integrity
• Works with Claude AND OpenAI — one review process covers safety for any LLM provider

Cross-References

• How to Add AI Agent Safety to Your CI/CD Pipeline
• How to Use Pre-Commit Hooks for AI Agent Safety
• Policy-as-Code Pattern
• How to Test AI Agent Safety Policies