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I have an idea on how to tell if a codebase is rotting under AI Agent maintenance. We can collect and analyze how the coding agent reads code during programming tasks, and see if the code access and token consumption are steadily increasing for similar development tasks. If the code readability doesn't degrade for the agent, the maintainability of the codebase should be fine.

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Mist of human written codebases are unusable for llm dev by that definition.

Turns out that if they're unusable by LLMs they're likely unusable by human devs. If you follow sane clean coding principles (like not having godclasses) it turns out coding agents (and humans!) can understand and navigate your codebase, especially if you use recognizable patterns, even with very light documentation.

One of these days you’ll learn about “enterprise” code

I have seen good enterprise code and bad enterprise code. Clean Code suggests progressive rewriting of bad code.

When you touch a file you have an opportunity for code clean up, add unit tests to ensure your changes break nothing, and refine the code.

Agree, agentic coding seem to have shifted the trade-off about over-engineering, I found clean architecture is a good practice for coding agent, so every task have a clean and limited context, only a few directly connected classes or interfaces is relevent to any local modification.

We judge long-term quality of human codebases (at least OS) by ongoing activity; for LLM codebases maybe a consistent or increasing level of activity is a bad smell?

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