Exam CCAR-F Topic 1 Question 35 Discussion
Actual exam question for Anthropic's CCAR-F exam
Question #: 35
Topic #: 1
Question #: 35
Topic #: 1
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.
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?
Suggested Answer: A Vote an answer
In the standard Claude tool-use loop, the application executes lookup_order and sends its output back as a tool_result . That result becomes part of the conversation state available to Claude. Claude then evaluates the purchase date, refund policy, customer request, authorization constraints, and available tools before selecting the next action.
Anthropic describes client-tool orchestration as a repeated loop: Claude emits a tool_use request, the application executes it, returns a tool_result , and Claude continues reasoning from the updated conversation.
Claude, rather than the tool implementation, selects when and how to invoke the next available tool unless the application has explicitly implemented a fixed workflow. ( https://platform.claude.com/docs/en/agents-and- tools/tool-use/how-tool-use-works ) Options B and C describe possible custom orchestration architectures, but neither is stated in the scenario.
Option D is inconsistent with adaptive agent behavior because later actions depend on information that did not exist before lookup_order completed. A rigid sequence would not respond appropriately to different purchase dates, eligibility states, or order conditions.
The tool result should return high-signal fields such as purchase date, return-window status, refund eligibility, existing refund status, and stable order identifiers so Claude can make the subsequent decision accurately.
Official references/topics: Tool-result continuation, adaptive agent loops, model-directed tool selection, sequential dependency handling.
Anthropic describes client-tool orchestration as a repeated loop: Claude emits a tool_use request, the application executes it, returns a tool_result , and Claude continues reasoning from the updated conversation.
Claude, rather than the tool implementation, selects when and how to invoke the next available tool unless the application has explicitly implemented a fixed workflow. ( https://platform.claude.com/docs/en/agents-and- tools/tool-use/how-tool-use-works ) Options B and C describe possible custom orchestration architectures, but neither is stated in the scenario.
Option D is inconsistent with adaptive agent behavior because later actions depend on information that did not exist before lookup_order completed. A rigid sequence would not respond appropriately to different purchase dates, eligibility states, or order conditions.
The tool result should return high-signal fields such as purchase date, return-window status, refund eligibility, existing refund status, and stable order identifiers so Claude can make the subsequent decision accurately.
Official references/topics: Tool-result continuation, adaptive agent loops, model-directed tool selection, sequential dependency handling.
by Griffith at Aug 05, 2026, 06:41 PM
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