Beyond Prompts: How Git Hooks Steer AI Coding Agents in Production

TLDR: At Fleek, both engineers and non-engineers build internal tools using AI coding agents. We found that instruction files alone aren't enough - the AI follows them most of the time, but not always. Git hooks gave us a deterministic enforcement layer on top. This post walks through how we designed them and why the error messages matter more than you think. The Problem We run an internal tools platform - a monorepo with 10+ sub-applications. The people building on it range from senior engineers to business team members who have never written code before and use Claude Code as their primary development tool. In a traditional engineering team, you enforce standards through code reviews and tribal knowledge. An engineer sees a failing hook and thinks: "Ah right, I need to fix that." They have context. They know the codebase. An AI agent has none of that. It doesn't know your conventions. And even if you spell everything out in an instruction file - it might still get it wrong. Heuristic