Any time the LLM can establish a tight feedback loop it can iterate effectively enough to make good progress.
Not any time. Your feedback loop must provide some direction so the iteration is at least heading towards an improvement. For novel applications you might not have a convenient way to do that yet.
Even with that provision there's still a possibility of incrementally updating output converging on a local maximum. That local maximum might not be a solution at all. If it is a solution then the model might still miss much better solutions that a real developer with more insight might find.
As you said yourself the models need external feedback if they don't have sufficient information to make adjustments by themselves. In some cases that feedback might be "Start again because you're hopelessly off track". Sometimes even when they start again they just get hopelessly off-track again. That can happen even if they are explicitly prompted to do something else. So even closely supervising the LLM and not using self-iterating agents isn't guaranteed to get good results for novel applications.
Not any time. Your feedback loop must provide some direction so the iteration is at least heading towards an improvement. For novel applications you might not have a convenient way to do that yet.
Even with that provision there's still a possibility of incrementally updating output converging on a local maximum. That local maximum might not be a solution at all. If it is a solution then the model might still miss much better solutions that a real developer with more insight might find.
As you said yourself the models need external feedback if they don't have sufficient information to make adjustments by themselves. In some cases that feedback might be "Start again because you're hopelessly off track". Sometimes even when they start again they just get hopelessly off-track again. That can happen even if they are explicitly prompted to do something else. So even closely supervising the LLM and not using self-iterating agents isn't guaranteed to get good results for novel applications.