AI Agent Builds Python Cache System Using Self-Correcting Feedback Loop, Reveals Current Limitations of Automated Coding

AI agent successfully builds complex Python cache system through innovative self-correcting feedback loop that automatically writes code, tests it, and fixes errors, but reveals current limitations including skipping critical features like thread safety due to token constraints.

• A developer demonstrates an AI feedback loop by building a Python two-tier cache system using Claude, where the AI agent follows a plan-do-verify cycle with automatic validation and self-correction
• The AI agent successfully implements most features by writing code and tests together, then running validation scripts to catch and fix errors like timestamp synchronization bugs before moving to the next step
• The project reveals both strengths and limitations of current AI coding workflows, including effective black-box testing approaches but also issues like the agent skipping thread safety implementation due to token concerns