Lessons Outlive Their Failures: A Case Study of Rules for AI Coding Agents

Hassan Hassanzadeh Aliabadi

AI coding agents increasingly work inside a harness: rule files such as AGENTS.md, on-demand skills, runtime hooks and automated checks. Most studies of harnesses look at a single point in time or at short benchmark runs. I report a six-week case study of my personal system of record, a plain-markdown repository that three AI agent tools edited daily under one shared rule set. I built a ledger of 118 agent failures, their fixes and whether they came back. I also tested the system's monthly LLM documentation audit against 47 known stale statements and 20 planted errors. Finally, I replayed five past failures in isolated git clones, with and without their fixes. All results come from one repository with three runs per condition, so I report them as exploratory. Stale documentation (drift) was the largest failure class (48 of 118), although 35 of those were found on one audit day. A single audit run found about half of the known drift (Claude Sonnet 5.5 mean: 50%), and at least 93% of its findings were correct. On Sonnet 5.5, the agent finished four of the five replayed tasks. In three of those four, the failure did not happen, with or without its fix. Only the stale-count failure…