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-tiny-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: First, I will filter the 6 available flights from London to Paris for tomorrow by price to find the cheapest ones. Then, I will select the top 3 cheapest options. Lastly, I will store the top 3 options in memory under key 'top_flights'. I follow these steps to ensure efficiency and effectiveness in my actions, as per the provided instructions. I always try to avoid redundant tool calls and store meaningful results in memory using the memory tool. I work in cycles and chain tools logically if needed.

Response: The next tool I will call is 'flights.filter_by_price' followed by 'flights.sort_by_duration' and 'flights.get_cheapest_option'. After that, I will use 'memory.store_item' with key 'top_flights' to store the top 3 cheapest flights.