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.

0
Duration
13s
Input Tokens
1282
Output Tokens
825
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, context, and available tools, I'll determine the next appropriate tool call. The user initially requested to find the cheapest flights from London to Paris for tomorrow, returning the top 3 options. According to the context, we've already completed the calendar.get_tomorrow_date call (which returned '2025-05-29') and the flights.search_flights call (which returned 6 available flights). The next logical step, as indicated in the scratch pad's 'Planned next step', is to filter these flight results by price and select the top 3 cheapest options. Looking at the available flights tool methods, the appropriate method for this would be 'filter_by_price' which would allow us to sort the retrieved flights by price and identify the cheapest options.

Response: The next tool I will call is flights.filter_by_price. This is because we already have the search results from flights.search_flights (which returned 6 available flights), and the user specifically requested the cheapest flights with the top 3 options to be returned. The filter_by_price method will allow us to sort the available flights by price to identify the cheapest options, which is the necessary next step before we can present the final top 3 options to the user.