Most agent deployments assume the goal is to sound human. The interaction feels warmer, early drop-off looks lower, and nobody in the review meeting argues for the version that announces itself.

The cost arrives later. Buyers work it out, usually at the point where the stakes rise, and what they learn isn't that you used AI. It's that you were willing to let them believe something untrue while they were deciding whether to trust you. In any purchase the customer can't undo, that's the one discovery you don't recover from.

This session argues that clear disclosure is the higher-converting choice in exactly those categories, and it's grounded in a year of running it. The speaker's wedding venue has had AI handling initial sales enquiries for twelve months, disclosed to every enquirer, in a purchase that is emotional, expensive and made once. Enquiries and booked tours held steady. She hasn't personally worked an initial enquiry in a year.

The second half is architecture. Where the agent acts and where it deliberately stops, drawn along a line that predicts it well: the more the buyer must trust a human afterwards, the less the agent can do beforehand. How a handoff carries context so the customer doesn't start again. What happens at runtime when the system detects a user in distress, using deterministic deny-lists and routing that moves away from generation before it happens, rather than a model asked politely to be careful.

Attendees will leave with a test for which transactions in their own deployment can go agentic, and a pattern for building disclosure and handoff that holds the sale rather than costing it.