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

1318

Output Tokens

301

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's ultimate goal is to find the cheapest flights from London to Paris for tomorrow and return the top 3 options.
2. The flights.search_flights call has already returned 6 available flights, which provides the raw flight data.
3. According to the scratch_pad, the planned next step is 'Filter by price and select top 3 cheapest options.'
4. Looking at the available tools, the flights tool has a 'filter_by_price' method that can help identify the cheapest options.
5. Rule #3 requires storing meaningful results in memory after every tool call, so after filtering, I would store the top 3 cheapest flights.
6. The logical progression is: search_flights → filter_by_price → store results → present to user.

Response: I will call the 'flights.filter_by_price' method next. This is because:
1. The user specifically requested the 'cheapest flights'
2. We already have the raw flight data from search_flights
3. The filter_by_price method will help identify the most affordable options
4. After filtering, I will store the top 3 cheapest flights in memory using memory.store_item as required by the rules
5. This aligns with the planned trajectory in the scratch_pad and moves us closer to the final goal of presenting the top 3 options to the user