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

37s

Input Tokens

1478

Output Tokens

391

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's request is to find the cheapest flights from London to Paris for tomorrow and return the top 3 options. The trajectory shows flights.search_flights has already been called and returned 6 flights. The planned next step is to filter by price and select the top 3 cheapest. The available flights tool has a method filter_by_price, which can be used to sort the flights by price. This is the logical next action before storing the results. No other tool is needed at this moment because the flights data has just been obtained and needs processing to fulfill the user's request.

Response: The next tool to call is the flights tool's filter_by_price method. This is because the user requested the cheapest flights, and filter_by_price will sort the 6 available flights by price, allowing us to identify and select the top 3 cheapest options, directly aligning with the planned next step in the trajectory.