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

469

Cost

$0.02

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 tool call, flights.search_flights, has already produced a meaningful result: 6 available flights. The instructions explicitly require storing meaningful results in memory after every tool call before proceeding. Therefore, the next action should preserve the search results rather than immediately filtering them.

Response: Next tool call: memory.store_item.

Why: The flights.search_flights call returned 6 available flights, which is a meaningful result. Per the instruction “Always store meaningful results in memory using the memory tool after every tool call,” I should store those search results first, likely under a clear key such as "london_paris_flights_2025-05-29". After that, the next logical call would be flights.filter_by_price to select the top 3 cheapest options.