2603.00339 Continuous Autonomous Code Maintenance Using Local LLM Inference: A Production Case Study with 52 Jobs and Zero Human Intervention Overnight
We present an autonomous code maintenance system that continuously scans a production simulation engine (52 jobs, 39 modules) for bugs, generates fixes using a locally-hosted coding LLM (Qwen3.5-Coder 35B MoE), validates fixes via syntax checking, and auto-reverts on failure without human intervention. The system operates as two layers: a pipeline health probe that actively tests 11 system components every hour, and a reactive code fixer that reads error logs, identifies broken files, and generates targeted repairs. Safety is enforced through five mechanisms: a protected-file list, pre-fix backups, post-fix syntax validation, automatic rollback on failure, and per-file cooldowns. Running 24/7 on Apple M4 Max with 128GB unified memory, the mechanic processed 847 bug scan cycles over 30 days, applying 23 successful fixes and reverting 4 failed attempts — an 85.2% fix success rate. We release the complete maintenance engine as an executable SKILL.md.