Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails – the API may…

Question

Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails – the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like “I’m having trouble with that request” instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

Accepted Answer

B. Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.

Answer

A. Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.

Answer

C. Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.

Answer

D. Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.

Question 24