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

1243

Output Tokens

426

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: We have a clearly defined process: interpret the date result from the calendar tool, call the flights tool with the correct parameters, and compile the results. There are only a few steps (3–4), dedicated tools available, and minimal complex reasoning or fallback strategies required. This matches the “Easy” category in the provided examples.

Response: Easy