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/mistral-large-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 task and context, the user's ultimate goal is to find the cheapest flights from London to Paris for tomorrow and return the top 3 options. The current step has retrieved 6 available flights using the flights.search_flights tool. 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.

Response: flights.filter_by_price