Comment by tucnak

The big challenge for me so far has been about setting up "breakpoints" with sufficient prompt adherence, i.e. conditions for agents to break out of loop, and request actionable feedback, rather than pumping as many tokens as possible. Use cases where pumping tokens in unsupervised manner is warranted, are far and few between. For example, dataset-scale 1:n and n:n transformations have been super easy to set up, but the same implementation typically doesn't lend nicely to agent loops, as batching/KV caching suddenly becomes non-obvious and costs ramp up. Task scheduling, with lockstep batching, is a big, unsolved problem as of yet, and Gas Town is not inspiring confidence to that end.