Rules to stop AI coding agents from breaking working code

The article addresses a real problem: as AI coding assistants become more capable within a single conversation, they also become more dangerous at scale. The author's observation—that after the first two weeks of apparent magic, the model begins fixing problem A while breaking problem B—reflects the gap between single-task performance and multi-file coherence. The proposed solution does not try to train the model better or prompt it harder; instead, it treats this as a structural problem that requires constraints.

The rules framework is grounded in a specific insight: vague instructions fail because they have no enforcement boundary. "Please be careful" or "write clean code" are aspirational but behaviorally empty. Prohibitions—"do not modify files I did not ask you to modify"—are actionable because they have a clear edge. The same logic extends to the five prompts: each one shifts the burden of verification away from the model's self-review (which inherits its own assumptions) onto external evidence. Requiring a test to pass, or a plan to be stated before execution, creates a checkpoint that the model cannot rationalize around using the same flawed logic that created the bug.

The author also identifies context decay as a practical failure mode. Long sessions accumulate abandoned directions and corrected mistakes, which stay in the context window and corrupt the signal. The solution—starting fresh every 3–5 tasks with a summary of what was tried and abandoned—is a form of state reset that prevents the model from re-proposing failed approaches. Finally, the reversibility principle (saving state before running agents, gating only irreversible actions) is a risk-management insight: it frees the model to iterate in low-stakes areas while maintaining human control over high-stakes decisions like deployment.