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

13s

Input Tokens

972

Output Tokens

273

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: Step 1: I have reviewed the user's request to find information about available flights from London to Paris for tomorrow. The context indicates that the date for 'tomorrow' has already been determined as '2025-05-29' using the calendar tool in a previous cycle.
Step 2: My task now is to find the available flights for the specified route and date. I have identified that the 'flights' tool is available and suitable for this purpose, as its description matches the requirement to find flight information.
Step 3: I will use the 'flights' tool with the parameters 'from_city' as 'London', 'to_city' as 'Paris', and 'date' as '2025-05-29'. This is the most direct and effective path to fulfill the user's request.
Step 4: There is no need for additional web searches or other tools at this stage since the flights tool is specifically designed for this task.
Conclusion: My next action is to call the 'flights' tool with the specified parameters to retrieve the flight information for the user.

Response: My next action is to use the 'flights' tool to find available flights from London to Paris on 2025-05-29.