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

1335

Output Tokens

206

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: Based on the context provided, the flights.search_flights call has returned 6 available flights from London to Paris for tomorrow. The user's request is to find the cheapest flights and return the top 3 options. The next logical step would be to filter these flights by price to identify the cheapest options. Therefore, the next tool I will call is the flights.filter_by_price method, as it will allow me to sort the flights based on their price and select the top 3 cheapest options. This aligns with the user's request and the planned next step in the scratch_pad.

Response: The next tool I will call is flights.filter_by_price. This is because the user's request is to find the cheapest flights from London to Paris for tomorrow and return the top 3 options. After retrieving the 6 available flights, filtering by price will allow me to identify and select the top 3 cheapest options, which aligns with the user's request and the planned next step.