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
10s
Input Tokens
1372
Output Tokens
145
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 wants to find the cheapest flights from London to Paris for tomorrow. The `flights.search_flights` tool has already been called and returned 6 available flights. The next step, according to the user's original request and the current trajectory, is to filter these flights by price and select the top 3 cheapest options. The `flights.filter_by_price` method is the appropriate tool for this task. After filtering, these results should be stored in memory.

Response: CALL: flights.filter_by_price(flights=[...], sort_by='price', max_results=3)