Broadly speaking, I find agentic coding to be even more exhausting (and expensive) than AI augmented completion. It's babysitting a mechanism that can screw up at any time, so it involves a *lot* more exhaustive work to make sure you have a harness to let the model try and retry with objective feedback very quickly. A continuing issue being that it fails to recognize an inability to do something but tries to do something anyway, which means any result that is not objectively measured in strict terms is at risk of weird drift.
Today I took a swing and it did a few weird things in my domain. Issue being here there's documentation of a standard but the very first and reference implementation deviated from the documentation, so every implementation was based on the reality rather than documentation. This particular scenario was very specific and you would have had to know very specifically about this risk to warn the model about that sort of mistake. It had a variety of legitimate inputs to process but dropped them without error because it didn't make sense and so it assumed malformed data.
It really depends on your inclinations and problem domain. If you are making a straightforward web ui interface to a fairly straightforward data set, then you can get pretty far. If you have requirements that aren't very picky and you can tolerate some drift, it can get pretty far. If you work in pretty esoteric spaces, then you have to either use it to accelerate and correct code by code or you have to spend a maddening amount of time describing all sorts of stuff to *not* do.
Completions are useful especially for boilerplate and such.