Opper

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.

Input Tokens

Output Tokens

Duration

Cost

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 need to systematically diagnose and address the issue:

1. Error Context Analysis:
- In this scenario, a previous tool call (calendar tool) returned an error
- The overall goal is to find flights from London to Paris for tomorrow
- Other available tools include 'flights' and 'web_search'

2. Troubleshooting Steps:
- First, verify the tools' current status (calendar tool shows as 'available' now)
- Check if the error was temporary or due to incorrect parameters
- Identify alternative approaches to obtain the required information

3. Next Action Decision:
- Since the calendar tool was previously unavailable, I'll attempt to use the flights tool directly
- I'll use the web_search tool to confirm tomorrow's exact date if needed
- Prepare to retry the tool call with precise parameters

4. Mitigation Strategy:
- Implement error handling by attempting the tool call again
- If repeated errors occur, switch to alternative information gathering methods
- Maintain focus on the user's original flight search request

Response: I will proceed by first using the web_search tool to confirm tomorrow's exact date, and then attempt to use the flights tool to search for available flights. If errors persist, I'll explore alternative methods to help the user find flight information.