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

976

Output Tokens

190

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: The user has requested information about available flights from London to Paris for tomorrow. In previous cycles, the user request was identified and the calendar tool was called to determine tomorrow's date, which was returned as '2025-05-29'. There is a 'flights' tool available which requires the parameters 'from_city', 'to_city', and 'date' in the correct format. All necessary parameters are now available: from_city = 'London', to_city = 'Paris', and date = '2025-05-29'. Therefore, the most logical and efficient next action is to call the flights tool with these parameters to retrieve the information the user requested.

Response: Call the 'flights' tool with parameters: from_city='London', to_city='Paris', date='2025-05-29' to find available flights for tomorrow.