Think of it in an end-to-end way: produce a ton of examples of final results of supervisor-worker agentic outputs and then train a model to predict those from the original user prompts straight away.

It's not true for all workflows. But many of today's custom workflows are like the magic "let's think step by step" prompt for the early LLMs. Low-hanging fruits, set to become redundant as better agentic capabilities are folded into the LLMs themselves.