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

18s

Input Tokens

3441

Output Tokens

879

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: Per instruction rule #3, I must always store meaningful results in memory after every tool call. The flights.search_flights call has just returned 6 flights, which are meaningful results that must be stored immediately using the memory.store_item method. Therefore, before proceeding to the filtering step mentioned in the scratch pad, I need to persist these 6 flight results with an appropriate key such as 'flight_search_results_london_paris'. After storing the raw results, I will then proceed to filter by price to identify the top 3 cheapest options as planned.

Response: The next tool to call is memory.store_item to store the 6 flight results returned by flights.search_flights, complying with the instruction to always store meaningful results in memory after every tool call.