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

1984

Output Tokens

232

Cost

$0.02

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: The task involves two clear steps: getting tomorrow's date via the calendar tool, then querying flights. Dedicated tools exist for each step, minimal reasoning is needed, and there is no adaptive planning or fallback required. This matches the 'Easy' example almost exactly.

Response: Easy — This task requires only a small number of clear, sequential steps (retrieve tomorrow's date, then query flights from London to Paris). Dedicated tools are available for each step (calendar and flights), the data flow between them is straightforward, and no complex reasoning, coordination across many tools, or fallback strategies are needed. It aligns directly with the canonical 'Easy' example.