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

15s

Input Tokens

1496

Output Tokens

1971

Cost

$0.01

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: Following the provided instructions, specifically rule #3, I must store meaningful results in memory after every tool call using the 'store_item' method. Since the previous action was 'flights.search_flights' and it successfully returned 6 flight options, the immediate next step is to store these results in the working memory. This ensures the data is preserved before performing further operations like filtering or sorting to find the top 3 cheapest options as requested by the user.

Response: The next tool I will call is 'memory' using the 'store_item' method. This is because Rule 3 of the instructions explicitly requires storing meaningful results in memory after every tool call. Since 'flights.search_flights' just returned 6 available flights, I must store this data before proceeding to filter or sort the results to identify the top 3 cheapest options for the user.