대규모 저장소에서 AI 코딩 에이전트가 실패하는 이유: 상태 비저장 컨텍스트 문제

Let’s be honest – stateless AI tools are incredibly powerful, but they have terrible short term memory, and are context-limited. They look at your repo through a keyhole — whatever’s visible in that one session is all they know.

You’ve probably seen your AI tool trying to fix one thing and break several others. This happens because they don’t know what else in your codebase depends on that particular module it is editing.

Modern codebases are deeply interconnected, and as repos grow, it gets harder for AI agents to track every dependency, architectural layer, and downstream effect.

I ran into this constantly while building a PR reviewer tool. Every time I asked AI to fix one thing or add a feature, it would quietly break something else. I wondered if it was possible to provide a complete dependency map to the entire codebase which can tell AI something like, “Hey, you just changed what this method returns, but you forgot about these 3 modules importing it”.

To fix this problem, I built a CLI which I call CXGRD . It maps your code, builds dependency graphs, calculates blast radius and provides enriched prompts for AI tools, while at the same time verifying the changes made by performing compiler-backed checks. It’s free to try — npm install -g cxgrd and run cxgrd scan on any repo.

Would genuinely love feedback from anyone who’s hit the same “fix one thing, break three” problem.

GitHub : https://github.com/cxgrd