One developer. No team. Just two AI coding agents running in parallel terminal sessions. Four months later: 81% PR acceptance, 91% test coverage, bugs going from report to merged fix in roughly thirty minutes. It wasn't a better model. It was what the codebase learned to measure. "The intelligence in an AI-assisted codebase lives less in the model and more in the loops the codebase wraps around it." What actually changed KubeStellar Console — a multi-cluster Kubernetes management dashboard in the CNCF Sandbox — was the proving ground. Five rungs of the AI Codebase Maturity Model emerged from that experience, tracing the path from agentic honeymoon to near-autonomous development loop: 1. Instructed — Externalise what you keep correcting: a CLAUDE.md , PR conventions, a rejection-reasons guide — together they covered ~90% of the reasons AI PRs were being rejected. 2.…