Scenario 1: Customer Support Resolution Agent

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.

Passage quoted verbatim from the official CCAR-F exam guide v1.0, section 5.

Primary domains

• Agentic Architecture & Orchestration
• Tool Design & MCP Integration
• Context Management & Reliability

What the exam asks inside this context

• Enforcement before money moves. Identity verification ahead of a refund is the canonical prerequisite-gate item, and the correct answer is programmatic every time. A refund above a threshold gets intercepted and rerouted to escalation, not talked out of.

• Tool descriptions with boundaries. The four named tools are deliberately close together, and questions turn on what a description fails to say — a threshold, an input format, an edge case.

• When to escalate. Three legitimate triggers: the customer asks for a human, policy is silent or has an exception, or the agent cannot make progress. Frustration is not one of them, and neither is a low self-reported confidence.

• Facts that survive a long conversation. Amounts, dates, order numbers and statuses belong in a persistent case-facts block outside the summarized history, because summarization is what eats them.