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

1m 22s

Input Tokens

1388

Output Tokens

1790

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 user wants the top 3 cheapest flights from London to Paris for tomorrow. The flights.search_flights call has returned 6 available flights. According to the scratch_pad, the planned next step is to 'Filter by price and select top 3 cheapest options.' The flights tool provides a filter_by_price method specifically for this purpose. Following the logical workflow and the explicit plan, we need to process the 6 search results to identify the cheapest flights before storing them in memory and presenting to the user.

Response: The next tool is flights.filter_by_price, because we need to filter the 6 search results by price to identify the cheapest flights and select the top 3 options as requested by the user.