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
3s
Input Tokens
1319
Output Tokens
285
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 task requires finding the cheapest flights from London to Paris for tomorrow and returning the top 3 options. The `flights.search_flights` call has already been executed and returned 6 available flights. The next logical step is to sort these flights by price to identify the cheapest ones. According to the instructions, I must store meaningful results in memory after each tool call. Therefore, after sorting, I will store the top 3 cheapest flights in memory using the `memory.store_item` tool. Since the Flights tool has a method `filter_by_price`, and given the trajectory's planned next step is to filter by price and select top 3, the most appropriate next tool call is `flights.filter_by_price` with a `max_results` parameter of 3. I will apply this method to obtain the cheapest flights before storing them in memory.

Response: The next tool I will call is `flights.filter_by_price` with parameters `from_city: 'London'`, `to_city: 'Paris'`, `date: '2025-05-29'`, and `max_results: 3`. This is because the task requires the top 3 cheapest flights, and filtering by price will allow me to retrieve the desired results before storing them in memory as per the instructions.