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

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

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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 user wants the cheapest flights from London to Paris for tomorrow and to receive the top 3 options. The flights.search_flights tool has already returned 6 available flights. According to the planned next step described in the context, the logical next tool to call is the flights.filter_by_price or flights.get_cheapest_option method to filter or select the cheapest flights from the retrieved results. This aligns with the user’s request and the instructions emphasize that after each tool call, results should be stored in memory. Therefore, the next tool call should filter or select the top 3 cheapest flights from the retrieved list before storing them in memory for final presentation to the user.

Response: The next tool to call is flights.filter_by_price or flights.get_cheapest_option to filter and select the top 3 cheapest flights from the 6 retrieved flights. This is necessary to meet the user’s requirement of finding the cheapest flights and to proceed logically before storing results in memory.