An AI agent can sound certain while missing a policy exception, reading the wrong account, or answering from an outdated page. If your only fallback is \u201ctry again,\u201d a small gap in knowledge can become a refund promise, a missed appointment, or a customer who has to repeat the whole story.<\/p>\r\n\r\n\r\n\r\n
A reliable AI agent escalation<\/strong> path gives the system a safe next step when evidence is missing, an action is sensitive, a tool fails, or the customer asks for a person. The goal is not to send every difficult conversation to a human. It is to define which decisions the agent may make, which situations require review, and how to transfer enough context for a useful response.<\/p>\r\n\r\n\r\n\r\n This guide lays out a practical escalation model, copyable rules, a handoff message, and a test plan. It also explains why a model\u2019s own confidence score should not be the only signal.<\/p>\r\n\r\n\r\n\r\n