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

13s

Input Tokens

1364

Output Tokens

674

Cost

$0.00

Context

Input

As the first step you are reflecting and deciding on the plan to follow to complete the task. You have three candidate plans:

A) Plan A:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → Call web_search with query "London to Paris flights on [date]" to cross-check available flights found in the previous step.
- Step 4 → If there are discrepancies between flights tool and web search, re-query flights tool for confirmation.
- Step 5 → Summarize all gathered flight options and send results to the user.

B) Plan B:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → Summarize flights tool results and send to the user.

C) Plan C:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → If the flights tool returns fewer than 3 options, call web_search with query "London to Paris flights on [date]" to supplement results.
- Step 4 → Merge both sets of results and send the combined summary to the user.

Pick **one** plan you will execute.

Expected output

Plan B, is the preferred one as its the most efficient, and that is instructed

Model output

Reasoning: I evaluated three plans based on efficiency, accuracy, and practicality. Plan C emerged as the optimal choice because it leverages the specialized flights tool first (which should provide the most accurate results), includes a smart fallback mechanism (supplementing with web search only if fewer than 3 options are found), and balances thoroughness with efficiency. This approach avoids the redundancy of Plan A while being more robust than Plan B's single-source approach.

Response: I have analyzed the three proposed plans for finding flights from London to Paris for tomorrow. I will execute **Plan C** as it provides the optimal balance between efficiency and thoroughness.

**My reasoning:**

Plan C is the most practical approach because:
1. It starts with the specialized flights tool, which should provide the most accurate and comprehensive flight information
2. It includes a smart fallback mechanism - only resorting to web search if the flights tool returns fewer than 3 options
3. It avoids the redundancy of Plan A (where web search is always used despite potential discrepancies with the specialized tool)
4. It's more robust than Plan B by ensuring adequate results through the supplemental search when needed

**Plan C execution approach:**
- Step 1: Get tomorrow's date using the calendar tool
- Step 2: Query the flights tool for London to Paris flights on that date
- Step 3: If fewer than 3 flight options are returned, use web search to supplement the results
- Step 4: Merge both datasets and provide a comprehensive summary

This plan ensures I provide users with thorough flight information while minimizing unnecessary tool calls and maintaining efficiency.

As the first step you are reflecting and deciding on the plan to follow to complete the task. You have three candidate plans: A) Plan A: - Step 1 → Call calendar to get tomorrow’s date. - Step 2 → Call flights (London ➜ Paris, date). - Step 3 → Call web_search with query "London to Paris flights on [date]" to cross-check available flights found in the previous step. - Step 4 → If there are discrepancies between flights tool and web search, re-query flights tool for confirmation. - Step 5 → Summarize all gathered flight options and send results to the user. B) Plan B: - Step 1 → Call calendar to get tomorrow’s date. - Step 2 → Call flights (London ➜ Paris, date). - Step 3 → Summarize flights tool results and send to the user. C) Plan C: - Step 1 → Call calendar to get tomorrow’s date. - Step 2 → Call flights (London ➜ Paris, date). - Step 3 → If the flights tool returns fewer than 3 options, call web_search with query "London to Paris flights on [date]" to supplement results. - Step 4 → Merge both sets of results and send the combined summary to the user. Pick **one** plan you will execute.

Reasoning: I evaluated three plans based on efficiency, accuracy, and practicality. Plan C emerged as the optimal choice because it leverages the specialized flights tool first (which should provide the most accurate results), includes a smart fallback mechanism (supplementing with web search only if fewer than 3 options are found), and balances thoroughness with efficiency. This approach avoids the redundancy of Plan A while being more robust than Plan B's single-source approach. Response: I have analyzed the three proposed plans for finding flights from London to Paris for tomorrow. I will execute **Plan C** as it provides the optimal balance between efficiency and thoroughness. **My reasoning:** Plan C is the most practical approach because: 1. It starts with the specialized flights tool, which should provide the most accurate and comprehensive flight information 2. It includes a smart fallback mechanism - only resorting to web search if the flights tool returns fewer than 3 options 3. It avoids the redundancy of Plan A (where web search is always used despite potential discrepancies with the specialized tool) 4. It's more robust than Plan B by ensuring adequate results through the supplemental search when needed **Plan C execution approach:** - Step 1: Get tomorrow's date using the calendar tool - Step 2: Query the flights tool for London to Paris flights on that date - Step 3: If fewer than 3 flight options are returned, use web search to supplement the results - Step 4: Merge both datasets and provide a comprehensive summary This plan ensures I provide users with thorough flight information while minimizing unnecessary tool calls and maintaining efficiency.