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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.

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn’t exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: { " isError " : true, " content " : [{ " type " : " text " , " text " : " Operation failed " }]} . The agent cannot distinguish between error types.

What’s the most effective improvement?

A.

Enhance error responses with structured metadata—include error_category (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.

B.

Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

C.

Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

D.

Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.

Anthropic CCAR-F Summary

  • Vendor: Anthropic
  • Product: CCAR-F
  • Update on: Aug 26, 2026
  • Questions: 152
Price: $52.5  $149.99
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