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

41s

Input Tokens

1349

Output Tokens

825

Cost

$0.00

Context

Input

What is the complexity of this task?

Task complexity refers to how challenging a task is for the agent to complete, based on the number of steps required, the need for reasoning or coordination, and the availability of tools.

Examples:
- Easy: A small number of clear steps; dedicated tools exist; minimal reasoning needed (e.g., find tomorrow's date and query flights).
- Medium: Several steps; requires combining many outputs from multiple tools (more than 4); moderate reasoning (e.g., summarize document changes across multiple files and generate a review).
- Complex: Many steps; adaptive planning; potential missing data or tool failures; fallback strategies required (e.g., plan a multi-leg trip with hotel, flight, visa checks, and dynamic adjustments).

Please select the appropriate complexity level for this task and explain why.

Expected output

This task is **easy**. It only requires determining tomorrow’s date and querying available flights — both of which have dedicated, reliable tools. There’s minimal reasoning

Model output

Reasoning: To determine the complexity, I analyzed the task described in the context: finding flights from London to Paris for tomorrow. The trajectory shows this involves: 1) Getting tomorrow's date (already completed using the calendar tool), 2) Querying the flights tool with the date and cities, and 3) Presenting results. This is explicitly a 4-step process according to the scratch pad ('Progress: Step 2 of 4'). The task uses dedicated, purpose-built tools (calendar and flights) that directly match the required actions. It requires minimal reasoning - just sequential tool execution without branching logic or combining multiple outputs. The task doesn't require adaptive planning, fallbacks, or coordination across more than 4 tools. This directly matches the 'Easy' example provided: 'find tomorrow's date and query flights.'

Response: Easy: This task involves a small number of clear, linear steps (4 total), uses dedicated tools that directly serve each step, and requires minimal reasoning. It matches the provided 'Easy' example almost exactly - just finding a date and querying flights. No complex coordination, adaptive planning, or fallback strategies are needed.

What is the complexity of this task? Task complexity refers to how challenging a task is for the agent to complete, based on the number of steps required, the need for reasoning or coordination, and the availability of tools. Examples: - Easy: A small number of clear steps; dedicated tools exist; minimal reasoning needed (e.g., find tomorrow's date and query flights). - Medium: Several steps; requires combining many outputs from multiple tools (more than 4); moderate reasoning (e.g., summarize document changes across multiple files and generate a review). - Complex: Many steps; adaptive planning; potential missing data or tool failures; fallback strategies required (e.g., plan a multi-leg trip with hotel, flight, visa checks, and dynamic adjustments). Please select the appropriate complexity level for this task and explain why.

Reasoning: To determine the complexity, I analyzed the task described in the context: finding flights from London to Paris for tomorrow. The trajectory shows this involves: 1) Getting tomorrow's date (already completed using the calendar tool), 2) Querying the flights tool with the date and cities, and 3) Presenting results. This is explicitly a 4-step process according to the scratch pad ('Progress: Step 2 of 4'). The task uses dedicated, purpose-built tools (calendar and flights) that directly match the required actions. It requires minimal reasoning - just sequential tool execution without branching logic or combining multiple outputs. The task doesn't require adaptive planning, fallbacks, or coordination across more than 4 tools. This directly matches the 'Easy' example provided: 'find tomorrow's date and query flights.' Response: Easy: This task involves a small number of clear, linear steps (4 total), uses dedicated tools that directly serve each step, and requires minimal reasoning. It matches the provided 'Easy' example almost exactly - just finding a date and querying flights. No complex coordination, adaptive planning, or fallback strategies are needed.