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 context, I need to analyze what the next step should be after already retrieving 6 available flights from London to Paris for tomorrow. The user's original request was to find the cheapest flights and return the top 3 options. According to the scratch_pad, the 'Planned next step' is explicitly stated as 'Filter by price and select top 3 cheapest options.' Looking at the available flight methods, we have several options including filter_by_price, sort_by_duration, get_cheapest_option, and get_flight_details. Since we need to identify the top 3 cheapest options from 6 results, the most logical next step is to use the filter_by_price method. This will allow us to filter the existing flight results by price to identify the cheapest options that match the user's request. After filtering by price, we can then select the top 3 options and proceed with storing them in memory as planned in the trajectory.

Response: I will call flights.filter_by_price to filter the 6 available flights by price to identify the top 3 cheapest options. This aligns with the planned next step in the trajectory and directly addresses the user's request for the cheapest flights from London to Paris for tomorrow.