AI agent handoff is the process of transferring a customer conversation from an AI system to a human agent, including the passing of context, conversation history, and relevant customer information.

Handoff is where many AI implementations fail the customer. Poor handoffs dump customers to agents without context, forcing repetition of information already provided. Good handoffs transfer full conversation history, customer identification, issue classification, and attempted resolution—everything the agent needs to continue seamlessly.

Effective handoff design requires decisions about: when to handoff (confidence thresholds, explicit requests, escalation triggers, sensitive topics), where to route (skill-based routing, priority queuing, relationship preservation), and what to transfer (conversation transcript, classified intent, customer data, suggested resolution). The handoff itself should be invisible to the customer or explicitly acknowledged ("I'm connecting you with a specialist who can help further").

For support operations, handoff rate and handoff quality are critical metrics. High handoff rates suggest the AI can't handle common issues. Poor handoff quality (agents asking customers to repeat themselves) erodes trust in the overall system. The goal: AI resolves what it can, hands off smoothly when it can't, and sets agents up for success rather than creating frustration for customers and staff alike.

Related terms: Human-in-the-loop, AI agent orchestration, Intelligent Virtual Agent

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# CX Metrics & KPIs