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.

Agents – openai/gpt-4.1-nano Task | Opper AI

Input Tokens

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Output Tokens

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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 indicates that the system has already retrieved 6 available flights from London to Paris for tomorrow via the 'flights.search_flights' call. The next logical step is to filter these flights to identify the top 3 cheapest options, aligning with the user's goal of finding the most affordable flights. Since filtering by price is a distinct operation available within the 'flights' tool, I will call 'filter_by_price' and specify 'max_results': 3 to get the top 3 cheapest flights. After this, I plan to store these results in memory under the key 'top_flights' so they can be retrieved later or used for presentation. This sequence is consistent with the user’s overall goal and instructions to store meaningful info after each step.

Response: Call the 'filter_by_price' method on the 'flights' tool with parameters including the previously obtained list of flights, such as specifying 'max_results': 3 to obtain the top three cheapest options, and then store these results in memory under the key 'top_flights'.