When working on more complex small-scale systems in Replit, the AI often applies quick fixes, repeatedly adding duplicate or near-duplicate code, or after refactoring, generating numerous backup files. These residual files interfere with the Agent’s operations, frequently misdirecting its patches and introducing new bugs—accumulating technical debt over time…
After extensive testing and research, I created a comprehensive, reusable workflow defined in Replit.md, complete with 
full rules and code samples so you can get started immediately 
. I named it 
TripleGuard Debug Framework and have published it at the URL (English version):
https://replit-agent-tripleguard-euw633j.gamma.site/
What can you do with this guide?
- Want to remove dead code and prevent AI patching blind spots? Deploy this workflow!
- On a team? Even beginners can follow the process to eliminate oversight risk.
- Primarily intended for Replit (
replit.md), but the concept can also be adapted for GitHub Copilot or other LLM Agents.
What does the framework achieve?
- Global execution rules — Limits AI modifications to only invoked, “live” code. Blind patches are automatically reversed.
- AI error-pattern cure roadmap — A full three-phase process: pre-checklist → patch strategy → run, validate, and update documentation.
- Tracepoint confirmation protocol — Only changes validated by actual trace logs are considered successful.
- If AI targets the wrong location, it automatically triggers a “real execution-path discovery” phase.
The guide includes real-world demos and visual walkthroughs. Anyone can follow step-by-step with reliable results.
Note: The TripleGuard Debug Framework is designed for monolithic or small web projects—it’s a lightweight debugging workflow and not suitable for distributed microservices architectures.