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

1282

Output Tokens

730

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 search_flights call has returned 6 available flights from London to Paris for tomorrow (May 29, 2025). Looking at our planned trajectory, we're at step 2 of 4 with 'Filter by price and select top 3 cheapest options' as the next step. The user specifically requested the cheapest options, so our next logical action is to use the filter_by_price method of the flights tool. This will allow us to identify the most affordable flights from the current results. After filtering, we'll need to store the top 3 options in memory as specified in our plan.

Response: The next tool I will call is flights.filter_by_price to filter the 6 available flights by price and identify the top 3 cheapest options as requested by the user.