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
17s
Input Tokens
1281
Output Tokens
275
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 flights.search_flights call has successfully returned 6 available flights from London to Paris for the specified date. The next logical step, given the user's request to find the cheapest flights and return the top 3 options, is to filter and sort these flights by price to identify the cheapest options. According to the context, the flights tool offers methods such as filter_by_price and get_cheapest_option. To comply with the instruction to always check available methods before use, I would retrieve the method list again, confirm the capability to filter/sort by price, and then proceed to filter the flights accordingly. Finally, after identifying the top 3 cheapest flights, I will store this result in memory with a clear key as required. Therefore, the next tool call will be flights.filter_by_price (or an equivalent method to get the cheapest options), because this allows me to narrow down from the 6 available flights to the 3 cheapest as requested by the user.

Response: The next tool I will call is flights.filter_by_price (or a similar flights tool method to get the cheapest flights), to filter and select the top 3 cheapest options from the 6 available flights. This step is necessary to fulfill the user's request of finding the cheapest flights and returning the top 3 choices.