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 – fireworks/deepseek-v3 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: The user's request is to find the cheapest flights from London to Paris for tomorrow and return the top 3 options. The task has progressed to the point where the 'flights.search_flights' call has returned 6 available flights. The next logical step, as indicated in the context's scratch_pad, is to filter these flights by price and select the top 3 cheapest options. Based on the tools available and the instructions, the most appropriate next action is to use the 'flights.filter_by_price' method. This method allows for filtering the search results by price, which aligns directly with the user's request for the cheapest options. After filtering, the results should be stored in memory using the 'memory.store_item' method with a clear key, as per the instructions to always store meaningful results in memory.

Response: The next tool to call is 'flights.filter_by_price' to filter the 6 available flights by price and select the top 3 cheapest options for the user.