Agents – novita/minimax-m2 Task

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

14s

Input Tokens

1389

Output Tokens

736

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: Looking at the context, the user wants the cheapest flights from London to Paris for tomorrow (2025-05-29) and wants the top 3 options. We have already completed the calendar check and flight search, which returned 6 available flights.

According to the instructions and the planned trajectory, the next step is to filter these 6 flights by price to get the cheapest 3 options. The flights tool has a 'filter_by_price' method available, which is perfect for this task.

After filtering to get the top 3 cheapest options, I should store these results in memory using the memory tool, as per rule 3 that states I should always store meaningful results in memory after every tool call.

The logical sequence is:
1. Use flights.filter_by_price to get the cheapest 3 options from the 6 available flights
2. Store the results in memory under a clear key like 'top_flights'
3. Then present the summary to the user

This approach follows the planned trajectory and uses the available tools efficiently to meet the user's requirements.

Response: The next tool I will call is `flights.filter_by_price` to filter the 6 available flights and get the cheapest 3 options. This is the logical next step because:

1. The user specifically requested the cheapest flights with top 3 options
2. We now have 6 available flights from the search results 
3. The flights tool has a 'filter_by_price' method that can narrow this down to the most affordable options
4. This follows the planned trajectory outlined in the scratch pad
5. After filtering, I'll store the results in memory as per the instructions

This approach efficiently progresses toward completing the user's request by systematically working through the available tools to find the most cost-effective flight options.

Reasoning: Looking at the context, the user wants the cheapest flights from London to Paris for tomorrow (2025-05-29) and wants the top 3 options. We have already completed the calendar check and flight search, which returned 6 available flights. According to the instructions and the planned trajectory, the next step is to filter these 6 flights by price to get the cheapest 3 options. The flights tool has a 'filter_by_price' method available, which is perfect for this task. After filtering to get the top 3 cheapest options, I should store these results in memory using the memory tool, as per rule 3 that states I should always store meaningful results in memory after every tool call. The logical sequence is: 1. Use flights.filter_by_price to get the cheapest 3 options from the 6 available flights 2. Store the results in memory under a clear key like 'top_flights' 3. Then present the summary to the user This approach follows the planned trajectory and uses the available tools efficiently to meet the user's requirements. Response: The next tool I will call is `flights.filter_by_price` to filter the 6 available flights and get the cheapest 3 options. This is the logical next step because: 1. The user specifically requested the cheapest flights with top 3 options 2. We now have 6 available flights from the search results 3. The flights tool has a 'filter_by_price' method that can narrow this down to the most affordable options 4. This follows the planned trajectory outlined in the scratch pad 5. After filtering, I'll store the results in memory as per the instructions This approach efficiently progresses toward completing the user's request by systematically working through the available tools to find the most cost-effective flight options.