Discover how Red Hat optimized for human maintainability and significantly increased AI-assisted productivity by formalizing architectural constraints into machine-readable rules, custom lint rules, and deep documentation. Learn about the three layers they built and the impact on development.

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Red Hat Developer

Red Hat's Hybrid Cloud Console team shares how they made their codebase AI-ready by formalizing architectural constraints into machine-readable rules. The approach uses three layers: an AGENTS.md file at the repo root encoding all architectural rules, custom ESLint rules enforcing boundaries (e.g., V1/V2 import isolation, required shared utilities), and 23 structured documentation files. When AI coding assistants (Cursor) automatically ingest AGENTS.md, they follow project-specific rules without guessing. The result: commit throughput jumped from ~12 to 53/month, a 9-month migration touched 5,000+ files, and first-try correctness improved dramatically. The key insight is that good governance for human engineers and good governance for AI assistants are identical — explicit rules, mechanical enforcement, and structured lookup-oriented docs benefit both audiences equally.

Engineering an AI-ready code base: Governance lessons from the Red Hat Hybrid Cloud Console

Scaling governance for humans and machines

Applying architectural constraints to AI execution

Best practices for starting your governance layer

<p>Turning architecture into machine-readable rules is such a big shift. It’s not just about helping AI—it forces teams to make implicit knowledge explicit, which is where most systems usually break down.</p>
