Opper

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.

Input Tokens

Output Tokens

Duration

Cost

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 provided context, I can see that the flights.search_flights call has already returned 6 available flights from London to Paris for tomorrow (2025-05-29). According to the trajectory and scratchpad, the user's ultimate goal is to find the cheapest flights and return the top 3 options. The planned next step mentioned in the scratchpad is 'Filter by price and select top 3 cheapest options.' Looking at the available methods in the flights tool, I need to identify which one would help achieve this goal. The available methods are: search_flights (already used), filter_by_price, sort_by_duration, get_cheapest_option, and get_flight_details. Since we need to find the cheapest options and select multiple flights (top 3), the most appropriate next tool call would be filter_by_price, which would allow us to filter the results by price criteria.

Response: The next tool I will call is flights.filter_by_price. This is because the user is looking for the cheapest flights from London to Paris for tomorrow and wants the top 3 options. After the initial search_flights call returned 6 available flights, the logical next step is to filter these results by price to identify the cheapest options, as outlined in the scratchpad's planned next step.