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

30s

Input Tokens

1542

Output Tokens

2967

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 previous action was `flights.search_flights`, which successfully returned 6 available flights. According to Instruction Rule #3 ('Always store meaningful results in memory using the memory tool. This means after every tool call, you store the results in memory'), the agent must persist this new data before performing any further processing or filtering. Although the scratch pad suggests filtering next, the strict operational rule to store results after every tool call takes precedence to ensure state preservation.

Response: I will call the 'memory' tool (specifically the 'store_item' method) to save the list of 6 available flights returned by the search. This adheres to Rule #3, ensuring the search results are persisted in the agent's state before proceeding to filter them for the top 3 cheapest options.