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

1542

Output Tokens

1337

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 for tomorrow. 
1. **Current Status:** The `flights.search_flights` call successfully returned 6 available flights. 
2. **Next Logical Step:** These 6 flights must be filtered or sorted to identify the 3 cheapest options. 
3. **Tool Selection:** The `flights` tool provides methods for manipulating flight data, specifically `filter_by_price`. 
4. **Action:** I will call `flights.filter_by_price` to process the 6 returned flights and select the 3 lowest-priced options. Although the exact parameters for `filter_by_price` are not shown, this method is the designated function within the toolset to handle price-based constraints and selection, which directly addresses the remaining task requirement.

Response: The next tool I will call is `flights.filter_by_price`. This is necessary to narrow down the 6 available flights to the top 3 cheapest options, thereby fulfilling the core requirement of the user's request.