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
1533
Output Tokens
343
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: Let's analyze the situation step by step:

1. Current status:
   - We've successfully searched flights from London to Paris for tomorrow
   - 6 available flights were returned
   - User wants the top 3 CHEAPEST options

2. Looking at available tools and methods:
   - flights tool has 'filter_by_price' and 'get_cheapest_option' methods
   - According to the instructions, we need to store results in memory

3. Next logical step analysis:
   - Since user wants cheapest flights, we should use flights.filter_by_price
   - This aligns with the planned next step in scratch_pad: 'Filter by price and select top 3 cheapest options'
   - Following rule #3, we'll need to store results in memory after filtering

4. Choice rationale:
   - filter_by_price is the most direct method to achieve our goal
   - This will help narrow down the 6 flights to the 3 cheapest options

Response: The next tool to call will be flights.filter_by_price because we need to find the 3 cheapest options from the 6 available flights. This directly addresses the user's requirement for the 'cheapest flights' and follows the planned sequence in the scratch pad. After filtering, we'll store the results in memory as per the instructions.