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

1439

Output Tokens

170

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 task and the context provided, the next logical step is to filter the 6 available flights by price to identify the top 3 cheapest options. This aligns with the user's request to find the cheapest flights from London to Paris for tomorrow. According to the planned next step in the scratch pad, the `flights.filter_by_price` method should be called to achieve this. After filtering, the results will need to be stored in memory as per the instructions.

Response: The next tool to call is `flights.filter_by_price` to filter the 6 available flights by price and select the top 3 cheapest options.