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.

Input Tokens

Output Tokens

Duration

Cost

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 task is to determine the next tool to call and the reasoning behind it, given that flights.search_flights has returned 6 available flights. The context reveals that the user wants the cheapest flights, and the planned next step is to filter by price and select the top 3 cheapest options. The instructions emphasize the importance of storing meaningful results in memory after each tool call. Therefore, I will use the flights.filter_by_price tool to narrow down the search, then store the results in memory.

Here's my thought process:
1.  The `flights.search_flights` tool has already been used.
2.  The next logical step, according to the plan, is to filter the results by price.
3.  The instructions state that after the call of a tool, I need to store the results. Therefore, I will then use the memory tool to store the results.

I will execute the `flights.filter_by_price` tool next.

Response: The next tool I will call is `flights.filter_by_price` to filter the flights by price and narrow down the available options. After this step, I will call the memory tool to store the results.