Usually the problems that cause this kind of thing are:

1) Bad prompt/context. No matter what the model is, the input determines the output. This is a really big subject as there's a ton of things you can do to help guide it or add guardrails, structure the planning/investigation, etc.

2) Misaligned model settings. If temperature/top_p/top_k are too high, you will get more hallucination and possibly loops. If they're too low, you don't get "interesting" enough results. Same for the repeat protection settings.

I'm not saying it didn't screw up, but it's not really the model's fault. Every model has the potential for this kind of behavior. It's our job to do a lot of stuff around it to make it less likely.

The agent harness is also a big part of it. Some agents have very specific restrictions built in, like max number of responses or response tokens, so you can prevent it from just going off on a random tangent forever.