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
12s
Input Tokens
975
Output Tokens
754
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: 1) I read the task and context: the user requested available flights from London to Paris for tomorrow. 2) Past trajectory shows we already called the calendar tool and obtained tomorrow's date: 2025-05-29. 3) Available tools include a 'flights' tool that takes from_city, to_city, and date—this is the direct, appropriate tool to find available flights. 4) The user gave no additional constraints (preferred airports, times, class, number of passengers), so the most direct next action is to search for all available flights for the specified route and date. 5) Plan: call the flights tool with from_city='London', to_city='Paris', date='2025-05-29', then present the returned options and ask any clarifying follow-ups if needed.

Response: Next action: call the 'flights' tool with parameters {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"} to retrieve available flights for tomorrow.