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 – mistral/magistral-medium-2506-eu 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: Based on the context provided, we are in the middle of a task to find the cheapest flights from London to Paris for tomorrow. We have already determined tomorrow's date and retrieved the available flights. The next logical step is to filter these flights by price to identify the top 3 cheapest options. This aligns with the planned next step mentioned in the context. Therefore, the next tool to call would be the `filter_by_price` method from the flights tool. This method will help us narrow down the list of flights based on price, which is the criteria specified by the user.

Additionally, the instructions emphasize the importance of storing meaningful results in memory. After filtering the flights by price, we will need to store the top 3 options in memory as per the planned next step.

In summary, the next tool to call is `flights.filter_by_price` to filter the available flights by price and then we will proceed to store the top 3 cheapest options in memory.

Response: flights.filter_by_price