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: The user's overall goal, as stated in the `trigger_content` and `trajectory`, is to 'find the cheapest flights from London to Paris for tomorrow, and return the top 3 options'. The `scratch_pad` further clarifies the `Planned next step` as 'Filter by price and select top 3 cheapest options.'

Currently, the `flights.search_flights` call has just returned 6 available flights. To achieve the user's goal of finding the *cheapest* flights and selecting the top 3, these returned flights need to be processed. The available `flights` tool methods include `filter_by_price`.

Therefore, the most logical and necessary next step is to use the `flights.filter_by_price` tool to process the 6 flights and identify the cheapest ones before selecting the top 3.

Response: The next tool to call is `flights.filter_by_price`. This is because the previous `flights.search_flights` call has returned a list of flights, and the user's request is to find the *cheapest* flights and return the top 3 options. The `filter_by_price` method in the `flights` tool is designed for this specific purpose, allowing us to process the returned flights to identify and select the cheapest options, aligning with the planned next step in the `scratch_pad`.