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

1534

Output Tokens

300

Cost

$0.00

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: Step-by-step thought process:
1. Review user goal: Find the cheapest flights from London to Paris for tomorrow (2025-05-29) and return the top 3 options.
2. Current status: flights.search_flights has returned 6 available flights. Trajectory confirms this was step 2.
3. Planned next step from scratch_pad: Filter by price and select top 3 cheapest options.
4. Available flights tool methods: filter_by_price, sort_by_duration, get_cheapest_option, get_flight_details. filter_by_price is ideal to get the cheapest ones from the 6 results.
5. After filtering, per rules, store results in memory (e.g., under 'top_flights').
6. Avoid redundant calls: No need to re-search flights.
7. Next logical action: Call flights.filter_by_price on the search results to get top 3 cheapest, then store.
Why: Directly advances to selecting top 3 cheapest as per user request and plan, optimizing for minimal actions.

Response: Next tool: flights.filter_by_price
Why: The search returned 6 flights. To fulfill the goal of finding the top 3 cheapest, filter the results by price (ascending) and limit to top 3. This chains logically from search_flights, avoids redundancy, and prepares for storing in memory before final summary.