I think they try pushing the models in a direction where they just go on until somehing is finished end-to-end. So basically building "the loop" into the model itself. E.g. with GPT-5.5 the model would often do only part of a job and get back to me with a recommendation for next steps and I would either say "Yes, go on" or "No, instead do X". And the newer models never even get back to me, they just keep on churning, deciding about the direction on their own. Sometimes they go into the right direction, sometimes they drift into something unrelated or just go down some rabbit holes for hours. I liked GPT-5.5 better, and I built my own tools around it to still get them to finish tasks end-to-end which works fine.