Apr 28, 2026
Set Up Policy as Code in 1 Hour (Control AI Code Fast)
If you want to start controlling AI-generated code today, this is the simplest way I’ve found to do it.
In the previous videos, I talked about why agentic development breaks at scale and introduced the concept of policy as code as a way to fix it. In this video, I’m showing how to actually get started.
The idea is straightforward. Instead of relying only on prompts, rules, or memory to guide AI, you introduce a deterministic layer that scans your codebase and flags violations. Think of it as a much more comprehensive, fully customizable linting system that works alongside tools like Claude.
What surprised me is how easy it is to get a first version working.
In this walkthrough, I show how you can go from zero to a basic policy as code setup in a very short amount of time. We start by generating a small set of rules, wire up a simple scanner, and immediately run it against a real codebase. Even with a basic setup, you’ll start catching issues and inconsistencies right away.
This is not the full system I use in production. At scale, this turns into hundreds or even thousands of rules, with more advanced concepts like evidence layers, caching, and reporting. But the goal of this video is to show that you don’t need any of that to begin.
If you’re using AI to write code and you’re starting to see drift, inconsistency, or quality issues over time, this is a practical way to start putting guardrails in place.
Over time, what I’ve found is that as you add more rules, the amount of drift drops significantly, and the system becomes more reliable without slowing development down.
If you haven’t watched the earlier videos in this series, I’d recommend starting with those for more context on why this approach exists and how it fits into a larger agentic workflow.
If you try this yourself, I’d be interested to hear what kinds of rules you end up writing and what it catches in your codebase.
Watch on YouTube →
