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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.

A customer returns 4 hours after their initial session about the same billing dispute. The previous 32-turn session contains lookup_order results showing “Status: PENDING, Expected resolution: 24–48 hours.” In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., “I see your refund is still being processed”) even after subsequent fresh tool calls return different information.

What approach most reliably handles returning customers?

A.

Resume with full history and configure the agent to automatically re-call all previously used tools at session start to ensure data freshness.

B.

Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.

C.

Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.

D.

Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

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