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

1m 2s

Input Tokens

1449

Output Tokens

4885

Cost

$0.02

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: The user requested the top 3 cheapest flights from London to Paris for tomorrow (2025-05-29). The flights.search_flights tool already returned 6 flights. According to the instructions, after every tool call, results must be stored in memory (rule 3). However, the scratch pad indicates the next step is to filter by price. To comply with both the plan and rules: 1) First, store the 6 flights in memory (if not already done). 2) Use flights.filter_by_price to process these flights, sort by price, and select the top 3. This avoids redundant searches (rule 4) and aligns with the planned workflow. The filter_by_price method is the logical choice to refine the existing results efficiently.

Response: The next tool to call is flights.filter_by_price. This will process the 6 retrieved flights, sort them by price, and select the top 3 cheapest options. After filtering, the results will be stored in memory under 'top_flights' per rule 3. This avoids redundant searches and directly addresses the user's request for the cheapest options.