I like the idea of predicting the user's response. How's this as an architecture for a helpful agent:

Given a user question, before you generate an answer you predict the user's ideal response to the model's answer (e.g. "thanks, that was helpful", or more likely a distribution over such responses), then generate an answer and iteratively optimize it to make the ideal user response more likely.

This way you're explicitly modeling the user's intent, and you can adapt the amount of computation appropriately for the complexity of the question by controlling the number of iterations on the answer.