How I Audit an Existing Codebase Before Letting AI Modify It
AI coding agents are powerful and dangerous on unfamiliar repos. Before I let an agent edit anything, I run a short audit. The goal is a shared map of the system so the agent (and I) do not “fix” production paths blindly.
Inspection-First Prompt
Inspect this project without modifying any files.
Identify:
1. Framework and versions
2. App entrypoints and deploy shape
3. Auth and session model
4. Main user-facing routes
5. API structure
6. Database layer and migrations
7. Background jobs / cron
8. Test commands
9. Obvious risks (secrets, destructive scripts)
Do not modify anything. Give a concise architecture summary first.What I Personally Verify
README,package.json/ lockfiles, Dockerfile, CI config- Env examples vs real secret handling (
.envnever committed) - How migrations run in prod
- Whether tests exist and how to run them
- Feature flags and plan/entitlement modules
Draw Hard Boundaries for the Agent
- Scope: one feature or bug, not “clean up the repo”
- Forbidden paths: prod configs, secrets, irreversible data scripts
- Required: show plan → wait for approval → then edit
- Required: run tests / typecheck after changes
- Required: summarize diff in human language
Risk Register Before Edits
[ ] Auth / billing / payments touched?
[ ] Migrations required?
[ ] Caching / CDN invalidation needed?
[ ] Multi-tenant isolation at risk?
[ ] Rollback plan clear?Preferred Agent Workflow
Inspect → Map → Plan → Approve → Edit → Test → Review diff → CommitFinal Thoughts
AI speed without a codebase audit produces confident wrong changes. Ten minutes of inspection-first work saves hours of revert-and-repair — especially on SaaS apps with billing and multi-tenant data.
Written by A.M. Rinas · Published August 27, 2026
