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

1164

Output Tokens

597

Cost

$0.00

Context

Input

Of the available tools. Which ones will you need to use to successfully complete the task?

Expected output

All tools will need to be used: calendar, flights,memory

Model output

Reasoning: Analyzing the task: find cheapest flights from London to Paris for tomorrow, return top 3 options. The user needs flights for 'tomorrow', which requires knowing the correct date first. 

Available tools:
1. **calendar** - Can get current date and calculate tomorrow's date using get_tomorrow_date method
2. **flights** - Can search flights (search_flights), filter by price (filter_by_price), and get cheapest options (get_cheapest_option)
3. **memory** - Can store results after tool calls as per instruction rule #3

Required tool chain:
1. First, use **calendar** tool to get tomorrow's date (needed for flights search)
2. Then use **flights** tool with parameters: from_city='London', to_city='Paris', date=[tomorrow's date], max_results=3
3. Finally use **memory** tool to store the results with clear keys

The flights tool supports all the requirements: searching flights between cities, filtering by price, and limiting results. No redundant tools needed.

Response: To complete this task, I will need to use:

1. **calendar** - To get tomorrow's date (required parameter for flights search)
2. **flights** - To search for London→Paris flights and get the cheapest options
3. **memory** - To store the flight results as per the instruction to store results after every tool call

Tool execution sequence: calendar → flights → memory