AI Coding Agents Need Enforcement Ladders, Not More Prompts

75% of AI coding models introduce regressions when maintaining codebases over time ( SWE-CI, arxiv 2603.03823 ). Not on one-shot fixes — those work. On sustained maintenance across 71 consecutive commits per task. And it gets worse: developers using AI coding assistants score 17% lower on conceptual understanding, code reading, and debugging assessments ( Anthropic, arxiv 2601.20245 ). Meanwhile, giving agents more freedom with tools outperforms pre-programmed pipelines by 10.7% ( Tsinghua, arxiv 2603.01853 ). The solution is not less autonomy. It is better enforcement around autonomous agents. The Root Cause: Prose Enforcement Fails Under Pressure Every AI team writes rules in markdown files. "Never modify production config." "Always run tests before committing." These are suggestions, not enforcement. When the context window fills up — and it always does — the model drops these rules first. The agent does not intentionally violate them; it simply forgets they exist. The Enforcement L