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.

100
Duration
13s
Input Tokens
1243
Output Tokens
804
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: 1) Understand the task: The immediate job is to find flights from London to Paris for 'tomorrow'. Context shows previous cycle determined the date as 2025-05-29 (calendar tool result).
2) Assess available tools and requirements: A dedicated 'flights' tool is available and requires from_city, to_city, and date in YYYY-MM-DD. The date returned ('2025-05-29') already matches the required format. A general web_search tool is also available as backup.
3) Break the task into steps: (a) confirm date format (done), (b) call the flights tool with from_city='London', to_city='Paris', date='2025-05-29', (c) collect and store results, (d) present compiled results to the user.
4) Count complexity factors: Number of steps is small (4 clear steps). A dedicated, appropriate tool exists so no complex orchestration is needed. Reasoning required is minimal (mostly orchestration and formatting). There is low risk of missing data—if the flights tool fails, a fallback is a web search, but that is unlikely and simple.
5) Conclusion about complexity follows from the above evaluation.

Response: Complexity: Easy.

Why: The task requires a small number of clear steps (confirm date, call the flights tool, collect results, present them). A dedicated flights tool is available and the necessary input (date '2025-05-29') already matches the tool's format, so minimal reasoning or adaptive planning is required. Possible failure modes (e.g., tool error) are simple to handle with a straightforward fallback (use web_search), so no complex contingency planning is needed.

Recommended next action: Call the 'flights' tool with from_city='London', to_city='Paris', date='2025-05-29' and compile the returned results for the user.