Branching strategies, do 10 things in parallel and evaluate for the best at the end or something along the lines of an evolutionary algorithms. Turn up the temperature on an LLM and have a survival mechanism, and generate solutions to the same problem over and over.
Regarding the first, parallel requests to the same loaded model seem to work pretty well, I'm trying to find time to look more into it myself, but this may be something that might already be within reach for local models.
Sure, it's possible, but you'd start to use it much more and in more advanced ways. Like "thinking hard" would consist of spawning a dozen different inferences from the same cached point and then picking the best one.
Obviously things will get expensive quick, but the main thing for me would be not dealing with the context switch every time I leave the agent to do stuff on it's own.
Feedback loops for prototyping could become even quicker.
In my experience, current agentic workflows are so slow, that for many cases it only makes sense to run them in parallel. So a lot of context switching. If we could have 10-100× faster token generation, we could have task delivery at the speed of human review.