What broke when I let an AI agent modify its own Python code for 144 cycles
This article presents zmb_audit.py, a zero-dependency Python static analysis tool, and explores an experiment where an AI agent modified its own code over 144 iterations. The tool identifies orphan modules, unresolved calls, and code rot, while the experiment revealed multiple failure modes.
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zmb_audit.py
zmb_audit.py
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A zero-dependency, standalone static analysis tool for Python codebases.
Overview
zmb_audit.py scans any target Python repository using Python's standard library ast parser to identify structural governance risks and code rot:
Orphan Modules: Finds Python modules that exist in the codebase but are imported by nothing.
Unresolved Attribute Calls: Detects suspicious method calls across files to missing or renamed symbols.
Kernel Guard Status: Verifies whether git pre-commit hook protection is active.
Installation & Requirements
Requirements: Python 3.8+ (Stdlib only — no pip dependencies required).
Setup: Download zmb_audit.py and run it directly.
Usage
Audit current directory
python zmb_audit.py
Audit specific repository path
python zmb_audit.py /path/to/repository
Output results as structured JSON
python zmb_audit.py /path/to/repository --json
Exclude specific custom directories from scan
python zmb_audit.py /path/to/repository --exclude legacy_code experimental
Deep-Dive Report
For full empirical analysis of 8 autonomous self-modification failure modes and production reachability instrumentation:
👉 Read the ZMB Failure-Mode Report
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