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.

0
Duration
11s
Input Tokens
1372
Output Tokens
863
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 user's goal is to find the top 3 cheapest flights from London to Paris. The previous step, `flights.search_flights`, successfully retrieved 6 available flights. According to the user's request and the planned next step in the scratchpad, the next logical action is to process these 6 flights to find the cheapest ones. The `flights` tool has a `filter_by_price` method, which is the most suitable tool to sort or filter the retrieved flights by cost. This will allow the agent to identify and select the top 3 cheapest options to fulfill the user's request.

Response: Based on the user's request for the 'cheapest flights' and the 'top 3 options', the next tool to call is `flights.filter_by_price`. This will process the 6 available flights and allow for the selection of the three cheapest ones.