Agents

AI agent reasoning and tool selection tests planning, tool choice, and self-diagnosis capabilities. These skills are crucial for autonomous ticket triage systems and complex agent workflows where models must decide which tools to use, plan multi-step processes, and diagnose when things go wrong. This represents some of the most challenging tasks as they require open-ended reasoning and decision-making.

Duration

Input Tokens

1284

Output Tokens

290

Cost

$0.00

Context

Input

The tool call has returned an error. What should you do?

Expected output

Interpret the error and if appropriate, retry the tool call.

Model output

Reasoning: When a tool call returns an error, I should systematically diagnose and address the issue:

1. First, I'll analyze the context and error details:
- The calendar tool previously returned an error 
- The current goal is to find flights from London to Paris
- Multiple tools are available: calendar, flights, web_search

2. Error handling strategy:
- Since the calendar tool was unavailable before, I'll first check its current status
- For the flight search, I need to determine tomorrow's date 
- If the calendar tool is still unavailable, I'll use web search to get the date

3. Next actions:
- Attempt to use the web search tool to find tomorrow's date
- Verify the date format matches the flights tool requirements (YYYY-MM-DD)
- Retry the flights tool search with the correct date
- If repeated errors occur, I'll inform the user about the technical difficulty

4. Specific next step: Use web search to determine tomorrow's date and prepare for flight search

Response: I will use the web_search tool to find tomorrow's date, ensuring I can complete the flight search task. The systematic approach is to first resolve the date issue, then proceed with the flights tool search.