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

1343

Output Tokens

644

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: We have already obtained a list of six flights via flights.search_flights. To achieve the user's goal of finding the cheapest flights, we must next filter that list by price. According to the instructions, we should first check the available methods of the flights tool, which includes filter_by_price. Calling this method will reduce the list to only those flights that meet the desired price criteria, after which we can use get_cheapest_option or further processing to pick the top three. This step follows the chain‑of‑tools logic, avoids redundant calls, and moves us closer to the final goal.

Response: The next tool call will be **flights.filter_by_price**. This call will filter the six flights by their price, allowing us to identify the cheapest options and ultimately select the top three cheapest flights from London to Paris for tomorrow.