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

12s

Input Tokens

1339

Output Tokens

228

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: Step 1: Understand the user's task - find the cheapest flights from London to Paris for tomorrow, top 3 options.
Step 2: Analyze available tools:
- calendar: Needed to retrieve tomorrow's date (e.g., get_tomorrow_date), as the date is relative ('tomorrow').
- flights: Essential for searching flights (search_flights with from_city='London', to_city='Paris', date=tomorrow), filtering by price (filter_by_price), getting cheapest (get_cheapest_option), and sorting to get top 3.
- memory: Required per instructions to store results after every tool call (e.g., store flight search results with clear keys).
Step 3: Logical flow - calendar → flights → memory. No other tools available or needed.
Step 4: All three tools are necessary for successful completion: resolve date, search/retrieve flights, store results.
Step 5: Avoid redundancy; this is minimal set.

Response: calendar, flights, memory