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
18s
Input Tokens
1281
Output Tokens
221
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 task requires finding the cheapest flights from London to Paris for tomorrow, and I have already used the 'flights.search_flights' tool to retrieve 6 available flights. According to the context and instructions, my next step should be aimed at filtering these 6 flights to find the 3 cheapest options. The current plan in the scratch pad indicates that filtering by price and selecting the top 3 cheapest options is the next logical step. The 'flights' tool has a method called 'filter_by_price', which is the appropriate choice for this task. After filtering, I must store the results using the 'memory' tool, as instructed. This aligns with the provided instruction to always store meaningful results in memory. Therefore, the next tool I will call is 'flights.filter_by_price' to filter the available flights by price.

Response: I will call the 'flights.filter_by_price' method next to filter the 6 available flights and find the top 3 cheapest options.