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

Input Tokens

1450

Output Tokens

155

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: The user requested the cheapest flights from London to Paris for tomorrow. The system has already retrieved 6 available flights using flights.search_flights. The next logical step is to filter these results by price to identify the cheapest options. Since the goal is to return the top 3 cheapest flights, the appropriate tool to call is filter_by_price with the retrieved flight data as input.

Response: The next tool to call is filter_by_price. This tool will process the 6 available flights, filter them by price, and return the cheapest 3 options. After filtering, the results will be stored in memory using the memory tool to ensure persistence for further steps.