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

1288

Output Tokens

190

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: 1. The user wants the cheapest flights from London to Paris for tomorrow and only the top 3 options.
2. The trajectory shows we have already called flights.search_flights and obtained 6 flights for 2025-05-29.
3. The next logical step is to filter these 6 flights by price and pick the 3 cheapest.
4. From the available flights tool methods, filter_by_price is the most appropriate next call to narrow down the results. After filtering, we can use the get_cheapest_option method or simply sort the filtered list and take the top 3.
5. We will then store these top 3 cheapest flights in memory as planned.
Therefore, the immediate next tool call is flights.filter_by_price (or a sequence of calls: filter_by_price followed by selecting the first 3 results).

Response: flights.filter_by_price