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

10s

Input Tokens

1746

Output Tokens

639

Cost

$0.02

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: The task asks which available tools are needed to complete the request: finding the cheapest flights from London to Paris for tomorrow and returning the top 3 options.

1. **calendar tool**: Needed to get tomorrow's date (using get_tomorrow_date method) since the user specified 'tomorrow'.
2. **flights tool**: Needed to search flights from London to Paris, filter/sort by price, and retrieve the cheapest top 3 options.
3. **memory tool**: Per the instructions, results from every tool call must be stored in memory, so this is also required.

All three available tools will be used in a logical chain: calendar → flights → memory (with memory used after each step).

Response: To successfully complete the task of finding the cheapest flights from London to Paris for tomorrow and returning the top 3 options, I will need to use all three available tools:

1. **calendar** — To determine tomorrow's date using the `get_tomorrow_date` method, since the user specified 'tomorrow' rather than an explicit date. This ensures the flight search uses the correct date.

2. **flights** — The core tool for the task. I'll use `search_flights` (from_city='London', to_city='Paris', date=<tomorrow>), then `filter_by_price` or `get_cheapest_option` to identify the top 3 cheapest flights, and potentially `get_flight_details` for richer info on each option.

3. **memory** — Per the operating rules, I must store meaningful results after every tool call using `store_item` with clear keys (e.g., 'tomorrow_date', 'flight_search_results', 'top_3_cheapest_flights'). This ensures state is preserved across the reasoning cycle.

**Logical chain:** calendar (get tomorrow's date) → memory (store date) → flights (search & find cheapest) → memory (store results) → return top 3 to user.