Yes, the prompt, and use of subagents is interesting. It could be characterized as tree of thoughts rather than "think step by step" chain of thoughts.

I see the need for this as coming down to two things:

1) LLMs are fundamentally prediction machines, and therefore ultimately will only do what they are prompted to do (and whatever that leads to). They may have been trained on, and/or have access to, all sorts of information that may be useful to solve a problem, but their predictive nature is to only use that information if explicitly prompted to, else it remains "dark" and inaccessible other than by luck. You're essentially having to tell the model "solve this problem using techniques A, B & C", otherwise techniques A, B & C will be off the radar unless the model already associates them to the problem.

2) The fundamental reason this sort of brute force tree-of-thoughts "explore all avenues" prompting is necessary, is because the model itself has no inherent curiosity to explore. Humans work differently. Our behavior is also prediction based, but we are also built for problem solving and continual exploration/learning via traits like curiosity (driven by prediction failure).

Problem solving via search can to some extent be prompted for, as here, or achieved via an external harness, but impasse resolution via curiosity, directed exploration and continual learning (if/when something new/unpredicted is encountered) is trickier. You can't usefully prompt a predictive model to "be curious" since that will only cause it to predict what a curious person would do, rather than the model being curious in reaction to the specific gaps in it's own knowledge.