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.

100
Duration
2m 12s
Input Tokens
1277
Output Tokens
266
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 task asks for the next tool I will call after the flights.search_flights call has returned 6 available flights, and why. Following the instructions, I must always store meaningful results in memory after every tool call using the memory tool's store_item method with clear keys. The flights.search_flights was the previous tool call, and it returned meaningful results (6 flights), so the immediate next step is to store these results in memory before proceeding to any further actions like filtering. This adheres to rule 3, ensures the data is preserved for subsequent processing (such as filtering by price as planned in the scratch_pad), and follows the logical chaining example in the instructions (calendar → flights → memory). After storing, I can then move to the planned next step of filtering by price to select the top 3 cheapest options, but the very next tool call is memory.

Response: The next tool I will call is the memory tool, to store the 6 available flights returned by the flights.search_flights call. This is because the instructions require always storing meaningful results in memory after every tool call, using store_item with a clear key like 'available_flights', to preserve the data for further processing and to follow the logical tool chaining.