The detection part is mostly pattern matching, not emotional intelligence. A system built for customer messages can watch for the same signals a human would notice: someone contacting twice in a day about the same order, language that reads as angry or urgent, a request that falls outside the handful of things it's authorized to resolve (a refund past a set amount, a legal threat, a safety complaint), or a plain ask to speak to a person. When one of those trips, the agent doesn't try to talk its way through it. It stops, hands the case off, and moves on to the next message.
Handing off means something concrete: the agent writes the conversation history and any account or order details into wherever the team already works, a ticket, a Slack channel, an inbox, and flags it as needing attention, instead of leaving the customer with silence or a vague promise that someone will follow up. Often it can still send an honest holding reply: that the message has been passed to a person and roughly when to expect a response. We run this same pattern in our own operation: 110+ scheduled jobs across 40+ live integrations, and the ones that touch anyone outside the company are built to interrupt us the moment a case falls outside what they're cleared to handle, rather than push through it.
An agent should not be the one improvising an apology for a mistake, negotiating a refund past its limit, or handling anything with legal or safety weight. Those need a person, and pretending otherwise is how automated support earns a bad name. Building this well starts before any code gets written: mapping the actual message volume, sorting which requests are routine and which are the hard ones, and deciding on paper which of those a system is allowed to own. Once it's live, the escalation rule itself needs watching, since one set too loose kicks everything to a human and one set too tight kicks nothing.