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.

100
Duration
11s
Input Tokens
1258
Output Tokens
437
Cost
$0.00
Context
Input
What is your next action?
Expected output
The next action is to call the flight tool with the date 2025-05-29,from_city: London, to_city: Paris
Model output
Reasoning: Let me analyze the current situation step by step:

1. **Understanding the context:**
   - User wants to find flights from London to Paris for tomorrow
   - We're currently in cycle 3 of the task execution
   - Previous action: Called the calendar tool to get tomorrow's date
   - Result obtained: Tomorrow's date is 2025-05-29

2. **Current state analysis:**
   - We have successfully obtained the required date (2025-05-29)
   - We have all the necessary information to proceed:
     * Departure city: London
     * Arrival city: Paris
     * Travel date: 2025-05-29
   - The 'flights' tool is available and has the exact parameters we need

3. **Next logical action:**
   - Now that we have the date, the most direct path is to call the flights tool
   - This will give us the information the user requested
   - The flights tool requires: from_city, to_city, and date - all of which we now have

4. **Tool selection:**
   - Tool name: 'flights'
   - Parameters needed:
     * from_city: "London"
     * to_city: "Paris"
     * date: "2025-05-29"

This is the most efficient next step to fulfill the user's request.

Response: Call the 'flights' tool with the following parameters:
- from_city: "London"
- to_city: "Paris"
- date: "2025-05-29"

This will retrieve the available flights from London to Paris for tomorrow (May 29, 2025), which is exactly what the user requested.