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 – groq/moonshotai/kimi-k2-instruct 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: The flights.search_flights call has already returned 6 flights. The user asked for the "cheapest" and wants the "top 3 options." Therefore, the logical next step is to filter/sort these results by price and pick the three cheapest. From the available flights-tool methods, `filter_by_price` or `get_cheapest_option` would be the most relevant. Since we need 3 options, not just one, the best fit is `filter_by_price` (or we could use `get_cheapest_option` three times if the tool supports limiting the number of results). Assuming `filter_by_price` allows us to limit to the top 3 cheapest, this is the tool to call next.

Response: flights.filter_by_price