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

2037

Output Tokens

372

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: Per rule 3, after every tool call I must store the results in memory. The flights.search_flights just returned 6 flights, so before any further processing (like filtering), I should store these raw results in memory using memory.store_item with a clear key such as 'search_results_london_paris_2025-05-29'. This preserves the data and follows the mandated workflow.

Response: The next tool I will call is **memory.store_item**. According to my operating rules, after every tool call I must persist meaningful results in working memory before moving on. The flights.search_flights call just returned 6 available London→Paris flights for 2025-05-29, so I will store them now — e.g., memory.store_item(key='flights_london_paris_2025-05-29', value=<serialized list of the 6 flights>). Only after this storage step will I proceed to the planned next action (flights.filter_by_price / get_cheapest_option to select the top 3 cheapest), which will then also be stored under a key like 'top_flights'. This keeps the chain auditable, avoids data loss, and complies with the 'store after every tool call' rule while preventing redundant re-queries.