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

21s

Input Tokens

1280

Output Tokens

575

Cost

$0.00

Context

Input

The flights.search_flights call has returned 6 available flights from London to Paris for tomorrow. What is the next tool you will call, and why?

Expected output

The next tool is memory, to store the flights tool call result.

Model output

Reasoning: We have retrieved 6 flights from London to Paris for 2025-05-29. The user wants the top 3 cheapest options. According to the tool definitions, the flights tool provides a filter_by_price method that can return the cheapest n results. Following the step-by-step plan and the instruction to always choose the minimal effective next action, we should call flights.filter_by_price with max_results=3 to get the three cheapest flights. This also aligns with our next planned step before storing results in memory.

Response: Call flights.filter_by_price with parameters {"from_city": "London", "to_city": "Paris", "date": "2025-05-29", "max_results": 3} to obtain the top 3 cheapest flight options.