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

1317

Output Tokens

181

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: 1. The user's goal is to find the cheapest flights from London to Paris for tomorrow and return the top 3 options. 2. The flights.search_flights call has already returned 6 available flights. 3. The next logical step is to filter these flights by price to identify the cheapest options. 4. The flights tool has a filter_by_price method, which can be used to achieve this. 5. Following the instructions, we should store the results in memory after filtering. 6. The planned steps in the context also align with this approach.

Response: The next tool to call is 'flights.filter_by_price' to filter the 6 available flights by price, followed by storing the top 3 cheapest options in memory using the 'memory.store_item' tool.