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
14s
Input Tokens
1281
Output Tokens
222
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 previous step involved calling the flights.search_flights method to retrieve available flights from London to Paris for the date 2025-05-29. Based on the instructions, after obtaining flight options, the next logical step is to filter these flights by price and select the top 3 cheapest options to fulfill the user's request. To do this, I need to call the 'filter_by_price' method from the flights tool, providing the retrieved flight data, and then sort the results accordingly. Since the search results are already in memory, I will first retrieve this data to apply filtering. Therefore, the next step is to call the 'retrieve_item' method of the memory tool to get the flight options stored under the key 'search_results' before filtering.

Response: The next tool I will call is the 'memory.retrieve_item' method to retrieve the flight options stored under the key 'search_results'. I will do this to access the data needed to filter the flights by price and select the top 3 cheapest options.