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

11s

Input Tokens

1372

Output Tokens

1514

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 tool call (flights.search_flights) returned 6 flights. The planned next step is to filter these flights by price to get the top 3 cheapest options. The flights tool has a 'filter_by_price' method that can sort and limit results. Since we have the flight data from the previous call, we should now call flights.filter_by_price with the list of flights and a parameter to get the 3 cheapest (e.g., max_results=3, order='ascending'). After that, we will store the top 3 in memory using memory.store_item. This follows the logical chain: search -> filter -> store.

Response: Next tool: flights.filter_by_price. Reason: We need to filter the 6 available flights by price to obtain the top 3 cheapest options, as planned in the scratch pad. This method will sort the flights by price in ascending order and return the lowest-priced ones. After filtering, we will store the result in memory.