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

1319

Output Tokens

242

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 to find the cheapest flights from London to Paris for tomorrow and return the top 3 options. So far, the following steps have been completed: 1) Determined tomorrow's date using `calendar.get_tomorrow_date`, which returned '2025-05-29'. 2) Searched for available flights using `flights.search_flights` with the date '2025-05-29', which returned 6 available flights. The next logical step, according to the plan, is to filter these flights by price to identify the cheapest ones. The `flights` tool has a method called `filter_by_price` that can accomplish this. After filtering, the top 3 cheapest flights can be selected and stored in memory.

Response: The next tool I will call is `flights.filter_by_price`. This is because the goal is to find the cheapest flights, and this tool allows us to filter the available flights by price. After filtering, we can select the top 3 cheapest options and proceed with storing them in memory.