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

200

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: Given the trajectory, flights.search_flights has already been executed for London → Paris on tomorrow’s date and returned 6 options. The user wants the cheapest top 3. The logical next tool is flights.filter_by_price to narrow results by cost before selecting top 3. This also aligns with the scratch pad plan (‘Filter by price and select top 3 cheapest options’). Per instruction #3, after that call we must store its output in memory, but the immediate next call is filtering.

Response: Next tool call: `flights.filter_by_price`.

Why: We already have 6 candidate flights from `flights.search_flights`, and the user explicitly asked for the **cheapest** options. The most direct next step is to filter/rank those results by price, then take the top 3. After this call, store the filtered result in memory (per rules) before presenting the final top 3 summary.