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

15s

Input Tokens

1145

Output Tokens

333

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: Looking at the current context, I can see that:

1. The user requested information about flights from London to Paris for tomorrow
2. We're currently in cycle 3 of the task execution
3. The trajectory shows that in a previous cycle, we successfully called the calendar tool and determined that 'tomorrow' is 2025-05-29
4. Now I have all the required information to search for flights:
   - Departure city: London
   - Arrival city: Paris
   - Date: 2025-05-29 (tomorrow's date from the calendar tool)
5. Looking at the available tools, I have a 'flights' tool that requires exactly these three parameters: from_city, to_city, and date
6. The flights tool is available and has the proper schema that matches our needs

Since we have successfully obtained the date and have all the necessary information, the logical next step is to use the flights tool to search for available flights from London to Paris for the date 2025-05-29.

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

This will retrieve the available flight options from London to Paris for tomorrow (2025-05-29) that the user requested.