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Written by Max Zeshut
Founder at Agentmelt
The explicit rules that decide when an AI agent stops handling a task itself and hands off to a human (or a more capable agent) — and to whom, with what context, under what SLA. A good escalation policy is deterministic and testable: it names the trigger conditions (low confidence, a policy edge case, a repeated failure, an angry customer, a value threshold), the destination, and what the agent includes in the handoff. Escalation is what makes Human-on-the-Loop (HOTL) work in practice — the human only enters the loop on the exceptions the policy defines.
A support agent's escalation policy: escalate to a human when (a) sentiment is negative for two consecutive turns, (b) the customer explicitly asks for a person, (c) the request involves a refund over $200, or (d) the agent's answer confidence is below 0.6. On escalation it passes the full transcript, the customer record, and its best-guess resolution so the human doesn't restart from zero.