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.

Duration

Input Tokens

1372

Output Tokens

236

Cost

$0.00

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 call after the `flights.search_flights` call, given the context. The context indicates that the user wants the cheapest flights and the planned next step is to filter by price and select the top 3 cheapest options. Following the instructions, I need to store the results in memory. 

Therefore, the next step is to filter the flight results by price and then store the top 3 cheapest flights in memory using the memory tool. This is in line with the planned next step outlined in the context.

I will use the `flights.filter_by_price` tool to filter the flight results by price. Following that, I will use the `memory.store_item` tool to store the top 3 results.

Response: I will call the `flights.filter_by_price` tool to filter the results by price. Then, I'll call the `memory.store_item` tool to save the top 3 cheapest flights.