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

11s

Input Tokens

1284

Output Tokens

323

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: Let's analyze the situation carefully:
1. We encountered an error with the calendar tool which was needed to determine tomorrow's date
2. Looking at the context:
   - The user needs flight information London → Paris for tomorrow
   - The calendar tool is marked as 'available' but returned an error
   - We're on cycle 3
   - We haven't exhausted retry attempts

3. Key considerations:
   - Calendar tool is crucial as we need the date for the flights tool
   - The error might be temporary
   - We have multiple options:
     a) Retry the calendar tool (since we haven't hit max retries)
     b) Try an alternative approach to get the date

4. Decision factors:
   - The tool is marked as 'available' suggesting it should work
   - This is likely the first attempt (based on trajectory)
   - A retry is the most direct solution before trying alternatives

Response: The next action should be to retry the calendar tool call. This is appropriate because:
1. The tool is marked as 'available'
2. We haven't exceeded the maximum retry limit (3 attempts)
3. It's the most direct path to get the required date information
4. If this retry fails, we still have additional retry attempts or can consider alternative approaches