Everyone is wiring LLMs into checkout flows right now. I want to make the unpopular case that for the decisions which actually move money - tax, discounts, eligibility, pricing - the model should never have the final say. Not because the models are bad, but because I have the benchmark data showing exactly what happens when they do, and a pattern that fixes it without throwing the LLM out. Here is the evidence, the one condition where it falls apart, and where it pays off.
TL;DR. The common advice - "put an AI agent in the loop" - is most dangerous exactly where it sounds most useful: the money path. Tax, promo eligibility, discount stacking, cart rules, pricing. On those decisions a probabilistic model is the wrong final authority. The pattern that holds up is a division of labour: the LLM formalizes the natural-language rules into a small, auditable specification, and a sound deterministic engine executes it. You stop reviewing code you cannot read and start approving a contract you can. I ran this across 113 experiments and nine model families, and the shape of the result is consistent. Here is the evidence, the one hard condition, and where it actually pays off.
The decision you should never let a model guess at
