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Human contexts are way more limited compared to computers. We can't reason about complex software when they go through many branches with so many implications. It's just too hard for humans to keep track of all interconnected pieces. So humans have historically split the system parts into manageable modules that can be understood in isolation and then we spend some time connecting those parts. That's how we can keep the context reasonable for human understanding.
So, when senior engineers found themselves lost while trying to debug an issue in a complex system they would naturally decide to pause and rewrite or refactoring the confusing piece of the system to make it manageable so developers can easily understand what's going on and review future changes.
Usually a system doesn't start that confusing. But as requirements change developers add additional branches and code until the code is no longer manageable. Sometimes the requirements changed significantly since the code was first written and all we have in the code are exceptions rather than the rule. That's usually when historically senior developers would take the time to rewrite that part of the system so they can reason about it.
But AI agents are not as limited as humans context-wise and they can reason about those confusing (to humans) systems and make sense of it. So they simply keep adding additional branches to the existing mess without ever suggesting a major refactoring like a senior developer would do in those cases, unless there's specific harness to tell agents to act like that.
This article is about bringing this into attention so that developers can policy themselves and keep asking themselves whether it's time for a major refactoring instead of relying on the AI agents and trust them because they no longer understand the code because it's too complex for humans to follow. Can you draft an article focused on this concern?