the one rule that makes a quoting agent trustworthy: the LLM never touches the number

Reddit r/AI_Agents News

Summary

A practitioner shares the architecture for trustworthy quoting agents: the LLM only extracts specs from messy customer messages at temperature 0, nulls become follow-up questions, pricing is done by a deterministic code engine with regression tests, and a human approves every quote before it goes out.

i build quoting agents for businesses that quote custom work. the biggest lesson has nothing to do with which model you use the obvious build is email in, LLM reads it, LLM spits out a quote. it looks great in a demo and it's dangerous in practice. a customer writes "20 posters for next week" or "quote for a 2 bedroom move" gives no real details and the model fills in the blanks and prices it. confidently. owners are scared of exactly that and they're right to be here's the architecture i use: the LLM only extracts. temperature 0, strict JSON. one job: pull the specs out of a messy message missing means null. if the customer didn't say it, the field stays empty. the model isn't allowed to fill it and every null becomes a flag nulls turn into questions. the agent drafts one reply asking for everything missing at once. a person checks it and sends pricing is plain code. a deterministic engine running the business's own rates. no model anywhere near the math price regression tests. fixed test jobs have to come out to the exact same price on every build. off by a cent and nothing ships a quote with nulls can't be marked as priced. the state machine blocks it a human approves everything before it reaches a customer once the model's job gets smaller the whole thing gets more useful. LLMs are great at reading messy human writing. they're bad at knowing what they don't know. so don't ask them to the hard part is never the AI. it's the domain. in print, "poster board" usually means foam board. in moving, "2 bedroom" tells you almost nothing without stairs, elevator and big items. every trade has its own vocabulary for what customers leave out and that's where the real work is the pattern holds for any business that quotes: extract, flag, ask, price with code, human approves if you're building agents for quotes, estimates or bids, curious whether you landed on the same split or found a safe way to let the model do more. and if you run a business that quotes custom work and want to know if this would help, happy to answer questions
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