Polite Isn't Enough: Your AI Agent Has to Follow the Policy

AI support-agent handoffs should be evaluated as policy-driven, multi-turn decisions rather than judged only by response relevance, politeness, or transfer rates. The discussion highlights three common failures: an agent delaying a required transfer by attempting self-service help, transferring a request it could have completed itself, and escalating before completing required steps such as retention offers or refund explanations; a fourth failure occurs when an agent never transfers a customer who needs human assistance. Effective policies specify each request type, prerequisite actions, whether the agent may complete the request, when it must transfer, and what context it should provide to the human agent. The proposed approach uses reviewer annotations to identify expected outcomes, custom metrics that inspect complete conversations and tool calls against explicit policy rules, production monitoring, simulations, and classifiers that route transfers for review. It argues that platforms such as Confident AI can support this workflow by turning reviewed failures into test cases and metrics, while cautioning teams against vague prompt-only rules, treating transfers as a default safe option, and testing only straightforward customer interactions.

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