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

1343

Output Tokens

273

Cost

$0.01

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: Analyze: We already called flights.search_flights and received 6 available flights. The user wants the top 3 cheapest options. Next we should operate on the returned set without re-querying (avoid redundancy). The Flights tool provides methods including filter_by_price and get_cheapest_option; to obtain the top 3 cheapest from 6, the logical next step is to filter/sort by price and take the first 3. Plan: Call a Flights tool method that ranks by price; filter_by_price is the closest fit given the method list. After that, per rules, store the filtered/sorted result in memory. Decide: Next tool call is flights.filter_by_price to extract the cheapest options from the 6-flight result, enabling selection of the top 3 for the user.

Response: Next tool call: `flights.filter_by_price`.
Why: The user asked for the top 3 cheapest flights, and we already have 6 results from `flights.search_flights`. The next logical step is to rank/filter those results by price (without re-running the same search), then select the first 3. After that, we would store the resulting top-3 list in `memory.store_item` (per the instruction to store results after each tool call).