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

1336

Output Tokens

490

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 previous step successfully retrieved 6 flights using flights.search_flights. According to the planned next step in the scratch pad, the immediate action is to filter these results by price to identify the cheapest options. The flights tool's filter_by_price method is the appropriate choice here, as it directly addresses the user's request for the cheapest flights. This step will reduce the 6 results to the most affordable options, enabling the subsequent selection of the top 3. The filter_by_price method requires no new parameters beyond the existing flight data, making it a logical continuation of the current workflow.

Response: flights.filter_by_price