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-small-eu Task | Opper AI

Input Tokens

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Output Tokens

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Duration

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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: As AgentX, I understand the user's request is to find the cheapest flights from London to Paris for tomorrow and return the top 3 options. According to the context, we have already determined tomorrow's date and searched for available flights.

My next tool to call will be 'flights' with the method 'filter_by_price' to filter the 6 available flights by price. Then, I will use 'flights' with the method 'sort_by_duration' to sort the top 3 cheapest flights by duration. Lastly, I will use 'memory' with the method 'store_item' to store the top 3 options in memory under the key 'top_flights'. This follows the plan stated in the scratch pad.

I am following rule 3 by storing meaningful results in memory after every tool call, and rule 6 by chaining tools logically (flights and memory).

Response: Filter the 6 available flights by price, sort the top 3 cheapest flights by duration, and store the top 3 options in memory under the key 'top_flights'.