Designing AI-Enforced Safeguards for Development

This isn’t magic. It’s thoughtful infrastructure.

After a few bugs slipped past my AI assistant, I paused and asked: “We’ve got docs, scripts, and checks… but which ones do I have to remember? Which ones does AI already know to run? And how many are just silently tucked into other commands?”

That turned into a real system redesign.

The Problem

149+ scattered NPM scripts. 4 inconsistent entry points: manual commands (npm run pre-deploy, etc.), AI-invoked checks (“Run the CORS validator”), auto-enforced (lint-staged, pre-commit), and hidden triggers (nested pre-* scripts).

Unclear boundaries. Easy to forget. Drift waiting to happen.

The Solution

One entry point: npm run safeguard --ai-mode

This command detects what kind of task is running, runs only relevant safeguards based on risk-based logic, outputs structured results for AI or human review, auto-fixes common issues, and stops the task if needed—or continues cleanly.

I also added --verify for post-task enforcement.

The Workflow

Every feature starts like this:

1. npm run safeguard --ai-mode
2. AI decides which checks apply
3. If broken → auto-fix or explain
4. If clean → proceed with implementation
5. After completion → --verify to confirm

Not a hypothetical. Not a vibes-based system. This runs in production.

The Shift

This is part of a bigger transition I’m chasing: from AI assistance to AI enforcement. Real safety nets. Real repeatability. So I can move faster without racking up tech debt.

I’m still tuning the risk-based logic—figuring out which safeguards are worth the overhead and which create more friction than they prevent. But the core insight feels right: if a check matters, it shouldn’t depend on me remembering to run it.