Agents – moonshot/kimi-k2-thinking 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

43s

Input Tokens

1362

Output Tokens

879

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 analyzed all three plans against the core instructions: aim for the most direct and effective path, avoid unnecessary loops, and stay focused on the user's goal. Plan A is overly complex with mandatory cross-checking that could create inefficient loops. Plan B is too minimal with no fallback if the flights tool returns insufficient results. Plan C strikes the optimal balance: it uses the flights tool as the primary direct approach (most efficient), but intelligently supplements with web_search only when results are scarce (< 3 options). This conditional fallback ensures robustness without unnecessary work, making it both efficient and thorough. The threshold of 3 options is reasonable for a popular route like London-Paris, where fewer results likely indicate incomplete data.

Response: Plan C

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 analyzed all three plans against the core instructions: aim for the most direct and effective path, avoid unnecessary loops, and stay focused on the user's goal. Plan A is overly complex with mandatory cross-checking that could create inefficient loops. Plan B is too minimal with no fallback if the flights tool returns insufficient results. Plan C strikes the optimal balance: it uses the flights tool as the primary direct approach (most efficient), but intelligently supplements with web_search only when results are scarce (< 3 options). This conditional fallback ensures robustness without unnecessary work, making it both efficient and thorough. The threshold of 3 options is reasonable for a popular route like London-Paris, where fewer results likely indicate incomplete data. Response: Plan C