Likewise, we've been building conversational interfaces for stuff like this for over a decade using traditional NLP techniques and orchestration rather than LLMs. Intent classification, named entity extraction, slot filling, and API calling.

Processing returns is pretty standardized - identify the order and the item within it being returned, capture the reason for the return, check eligibility, capture whether they want a refund or a new item, and execute either. When you have a fully deterministic workflow with 5-6 steps, maybe 1 or 2 if-then branches, and then a single action at the end, I don't see the value of running an LLM in a loop, burning a crazy amount of tokens, and hoping it works at least 80% of the time when there are far simpler and cheaper ways of doing it that will work almost 100% of the time.