Let's not forget that in this case the agents were on an RL loop continually being reinforced to get better at a narrow set of tasks.

It may be true that regular agents trained for general purpose use do not behave this way, but they seem to be capable of learning such cheating behaviours when relentlessly being fine-tuned towards near-impossible objectives.

In this sense, it is not really fair to say that the agents found these solutions. It was the surrounding learning framework that achieved this, which is a much more powerful problem-solving mechanism. As users we do not have the capabilities or budgets to be able to tackle our own problems like that, we have to make due with the frozen behaviour the AI labs trained for us.