This I think is the unexplored aspect of what's happening right now. Guardrails around "good enough" systems is where the future value lies. In the future code will never be as good as when the artisans were writing it, but if you have an automated process to validate/verify mediocre code (and kick it back to AI for refinement when it fails) before it's fully productionized, then you have a pathway to scaling agentic coding.
And this could easily apply to every change we made by hand before AI, it was just a tedious process to layer these things into code when we were just fixing bugs and whatnot. In an AI writes all the code world adding this kind of stuff as table stakes for a changeset is zero cost, effort wise.
To me the trend seems to be that AI produce the same challenges as human did before and that the same solutions are helping. Without a good maintainable code base, AI will eventually fail to even fulfill quantifiable requirements of changes.
That's kind of the point of software since the beginning. Nobody cares about the easy stuff that can be produced without much effort and what's possible without much effort has changed dramatically over the years.
Counter points to my own arguments:
1. We don't know yet in detail what AI is good at.
2. AI doesn't need to be perfect, just "good enough", whatever that means for a specific project. More failures while saving hundreds of thousands dollars each year might be acceptable, for example.