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Developer Builds Deterministic Feedback Loop to Catch AI Output Failures

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A junior undergraduate developer created a closed-loop agent configuration system to address unreliable AI outputs, such as files not being written despite the AI claiming completion. The system uses Python standard-library scripts to check file timestamps, exit codes, and flag files mechanically, while reserving content regeneration for the AI itself. All data is stored locally as Markdown and JSON files, making it fully offline-capable and Git-auditable with no external dependencies. The developer extracted one module from the system and submitted it to a major open-source repository with over 100,000 stars, where it was reviewed, approved, and merged after catching nine previously undetected issues. The project reflects a broader principle: deterministic script-based checks, not prompt engineering, are the reliable safeguard against probabilistic AI behavior.

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