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Ai Code Governance And Agent Architecture

Learn to govern the code AI writes and to architect the systems where agents modify software autonomously. When AI generates a huge fraction of the code and some agents can change code and infrastructure without direct supervision, speed without governance produces architectural drift and an enormous blast radius. This guide teaches you to build fitness functions that block any change — human or agent — that violates architecture rules in CI, to design a least-privilege permission model that limits what each agent can touch, and to route changes by risk tier with human-in-the-loop gates: trivial changes go through automatically, while changes touching payments, auth, or migrations require human approval. It covers tracking the provenance of AI-generated code for accountability and auditing, and encoding architecture as policy-as-code that an agent can't bypass, even if it tries to ignore it. Everything is simulated and executed in Python — the agent and its diffs are stubs; you never run a real agent or call an API. You work on Mercado's team, which already uses code agents to open half of its PRs, and close by designing the full governance layer for its AI contributions.