AI coding tools are widely adopted, but most teams plateau at prompt-and-review without a framework for systematic progression. This paper presents the AI Codebase Maturity Model (ACMM), a 6-level framework describing how codebases evolve from basic AI-assisted coding to fully autonomous systems. Inspired by CMMI, each level is defined by its feedback loop topology - the specific mechanisms that must exist before the next level becomes possible. I validate the model through a 100-day experience report maintaining KubeStellar Console, a CNCF Kubernetes dashboard built from scratch with Claude Code (Opus) and GitHub Copilot, and through the initial production deployment of Hive - an open-source multi-agent orchestration system that realizes Level 6: full autonomy. The system currently operates with 74 CI/CD workflows, 32 nightly test suites, 91% code coverage, and achieves bug-to-fix times under 30 minutes - 24 hours a day. The central finding: the intelligence of an AI-driven development system resides not in the AI model itself, but in the infrastructure of instructions, tests, metrics, and feedback loops that surround it. You cannot skip levels, and at each level, the thing that unlocks the next one is another feedback mechanism. Testing - the volume of test cases, the coverage thresholds, and the reliability of test execution - proved to be the single most important investment in the entire journey. v2 extends the model from 5 to 6 levels, adding Level 6 (Fully Autonomous) with Hive as reference implementation and Beads for cross-agent memory continuity, plus throughput acceleration data showing 5x PR throughput and 37x issue throughput from Level 2 to Level 6.

翻译：AI编程工具已被广泛采用，但大多数团队停留在“提示-审查”阶段，缺乏系统性进化的框架。本文提出AI代码库成熟度模型（ACMM），这是一个描述代码库从基础AI辅助编程演进至完全自主系统的六级框架。受CMMI启发，每一级由其反馈回路拓扑——即进入下一级前必须存在的特定机制——定义。我通过100天维护KubeStellar Console（一款从零使用Claude Code（Opus）和GitHub Copilot构建的CNCF Kubernetes仪表盘）的经验报告，以及实现第六级（完全自主）的开源多智能体编排系统Hive的初始生产部署，验证了该模型。该系统目前运行74个CI/CD工作流、32个夜间测试套件，代码覆盖率达91%，且实现每日24小时bug修复时间不超过30分钟。核心发现：AI驱动开发系统的智能不在于AI模型本身，而在于其周围构建的指令、测试、指标和反馈回路基础设施。你不能跳过任何级别，而在每一级，解锁下一级的关键正是另一个反馈机制。测试——测试用例数量、覆盖率阈值及测试执行可靠性——被证明是整个旅程中最重要的投入。v2版将模型从5级扩展至6级，新增第六级（完全自主），以Hive作为参考实现，并引入Beads实现跨智能体记忆连续性，同时给出吞吐量加速数据：从第二级到第六级，PR吞吐量提升5倍，问题吞吐量提升37倍。