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You are building a customer support resolution agent using the Claude Agent SDK.

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.

You’re implementing the escalation logic for when the agent should call escalate_to_human . Your team proposes four different approaches for triggering escalation.

Which approach will most reliably identify cases that genuinely require human intervention?

A.

Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.

B.

Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.

C.

Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer’s stated issue, ensuring a reasonable attempt before involving a human.

D.

Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and triggers escalation when the frustration score exceeds a configured threshold.

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