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 task, the user is interested in identifying the cheapest flights from London to Paris for tomorrow, and the task has already identified available flights. The next logical step is to sort these available flights by price to retrieve the top 3 cheapest options. According to the predefined usage pattern and available methods of the flights tool, I can use 'filter_by_price' to organize the flights by their cost. Once the flights are filtered, I will proceed to store the top 3 options in memory for later retrieval. This approach follows the cycle of analyzing the task requirements, planning the action pipeline, and executing the task as defined in the instructions, ensuring efficiency and adherence to the defined workflow.

Response: I will call the 'flights.filter_by_price' method next. This will allow the extraction of the top 3 cheapest options from the available flights between London and Paris for the target date. After retrieving the sorted information, I will store these results in memory using the 'memory.store_item' method, as instructed in the workflow plan.