Invariants must be documented as part of defining the data or program module, and ideally they should be restated at any place they're being relied upon. If you fail to do so, that's a major failure of modularity and it's completely foreseeable that you'll have trouble evolving that code.
Most importantly, this isn't hypothetical. We see that agents write programs that after some number of changes just collapse because they don't converge. They don't transition well between layers of abstractions, so they build contingencies into multiple layers, and the result is that after some time the codebase is just broken beyond repair and no changes can be made without breaking something (and because of all the contingencies, reproducing the breakage can be hard). This is why agents don't succeed in building even something as simple as a workable C compiler even with a full spec and thousands of human-written tests.
If the agents could code well, no one would be complaining. People complain because agent code becomes structurally unsound over time, and then it's only a matter of time until it collapses. Every fix and change you make without super careful supervision has a high chance of weakening the structure.
Neither do people, yet people manage to write software that they can evolve over a long time, and agents have yet to do that. I think it's because people can move back and forth between levels of abstraction, and they know when it's best to do it, but agents seem to have a really hard time doing that.
On the other hand, agents are very good at debugging complex bugs that span many parts of the codebase, and they manage to do that even with their context limitations. So I don't think it's about context. They're just not smart enough to write stable code yet.
You need a specific methodology to do that, one that separates "programming in the large" (the interaction across program modules) from "programming in the small" within a single, completely surveyable module. In an agentic context, "surveyable" code realistically has to imply a manageable size relative to the agent's context. If the abstraction boundaries across modules leak in a major way (including due to undocumented or casually broken invariants) that's a bit of a disaster, especially wrt. evolvability.
But this happens at all levels because there are many more than just two abstraction levels. E.g. do I change a subroutine's signature or do I change the callsite? Agents get it wrong. A lot.
Another thing they just don't get (because they're so focused on task success) is that it's very often better to let things go wrong in a way that could inform changes rather than get things to "work" in a way that hides the problem. One of the reasons agent code needs to be reviewed even more carefully than human code is that they're really good at hiding issues with potentially catastrophic consequences.
That's realistically because they aren't even trying to answer that question by thinking sensibly about the code. Working in a limited context with anything they do leaves them guessing and trying the first thing that might work. That's why they generally do a bit better when you explicitly ask them to reverse engineer/document a design of some existing codebase: that's a problem that at least involves an explicit requirement to comprehensively survey the code, figure out what part matters, etc. They can't be expected to do that as a default. It's not even a limitation of existing models, it's quite inherent to how they're architected.
It's funny, but one of the lessons I've learnt working with agents is just how much design matters in software and isn't just a matter of craftsmenship pride. When you see the codebase implode after the tenth new feature and realise it has to be scrapped because neither human nor AI can salvage it, the importance of design becomes palpable. Before agents it was hard to see because few people write code like that (just as no one would think to make a curtain rod load-bearing when building a structure).
And let's not forget that the models hallucinate. Just now I was discussing architecture with Codex, and what it says sounds plausible, but it's wrong in subtle and important ways.
I mean, if you properly define "do well" as getting a first draft of something interesting that might or might not be a step towards a solution, that's not completely wrong. A pass/fail test is verified feedback of a sort, that the AI can then do quick iteration on. It's just very wrong to expect that you can get away with only checking for passing tests and not even loosely survey what the AI generated (which is invariably what people do when they submit a bunch of vibe-coded pull requests that are 10k lines each or more, and call that a "gain" in productivity).
I think that the real two camps here are those who haven't carefully - and I mean really carefully - reviewed the code the agents write and haven't put their process under some real stress test vs those who have. Obviously, people who don't look for the time bombs naturally think everything is fine. That's how time bombs work.
I can make this more concrete. The program wants to depend on some invariant, say that a particular list is always sorted, and the code maintains it by always inserting elements in the right place in the list. Other code that needs to search for an element depends on that invariant. Then it turns out that under some conditions - due to concurrency, say - an element is inserted in the wrong place and the list isn't sorted, so one of the places that tries to find an element in the list fails to find it. At that point, it's a coin toss of whether the agent will fix the insertion or the search. If it fixes the search, the bug is still there for all the other consumers of the list, but the testing didn't catch that. Then what happens is that, with further changes, depending on their scope, you find that some new code depends on the intended invariant and some doesn't. After several such splits and several failed invariants, the program ends up in a place that nothing can be done to fix a bug. If the project is "done" before that happens - you're in luck; if not, you're in deep, deep trouble. But right up until that point, unless you very carefully review the code (because the agents are really good at making code seem reasonable under cursory scrutiny), you think everything is fine. Unless you go looking for cracks, every building seems stable until some catastrophic failure, and AI-generated code is full of cracks that are just waiting for the right weight distribution to break open and collapse.
So it sounds to me that the people you think are in the first camp not only just care how the building is built as long as it doesn't collapse, but also believe that if it hasn't collapsed yet it must be stable. The first part is, indeed, a matter of perspective, but the second part is just wrong (not just in principle but also when you actually see the AI's full-of-cracks code).