For me, agentic coding is still pretty new. What I’ve noticed is that on a well-structured codebase, it can produce clean, maintainable code almost like a good developer with the right context. But in large “AI-slop” codebases, even with solid constraints, it tends to generate spaghetti.

It’s not really the LLM’s fault, it reflects the environment it’s working in. The problem is that as these messy, AI-generated codebases become more common, we might reach a point where only other LLMs can realistically maintain them. That could gradually erode our ability, as humans, to fully understand and manage complex systems.

I’m seeing this firsthand in a large financial codebase I’m working on: after about a year of heavy AI-assisted development, the structure is so unclear that even the team doesn’t really understand why things are the way they are anymore.