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

18s

Input Tokens

1486

Output Tokens

265

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: The task in the context is to find available flights from London to Paris for tomorrow. This involves: 1) Determining tomorrow's date using the calendar tool (already done, result: '2025-05-29'). 2) Querying the flights tool with from_city='London', to_city='Paris', date='2025-05-29'. 3) Compiling and presenting the results. This matches the 'Easy' example exactly: 'find tomorrow's date and query flights'. It requires a small number of clear steps (2-4 total), dedicated tools (calendar and flights are available and directly applicable), and minimal reasoning—no adaptive planning, no multiple tool combinations beyond 2, no potential failures or fallbacks mentioned, no missing data. Current progress is step 2 of 4, straightforward progression. No need for web_search or complex coordination.

Response: Easy

This task is Easy because it involves a small number of clear steps: retrieve tomorrow's date (already completed via calendar tool) and query the dedicated flights tool with the exact parameters needed. Minimal reasoning is required, as tools are available and directly match the needs, with no adaptive planning, multiple leg coordination, or fallback strategies necessary. It aligns precisely with the provided Easy example.