Skip to main content
The Output Node marks the end of your agent’s execution. When your agent reaches an Output Node, it:

End Execution

Terminates the workflow and closes the browser

Outcome Label

Assign a custom, meaningful end-state

Structured Result

Return JSON data using a custom schema

Workflow Clarity

Clearly define how and why a run finished
Every workflow should have at least one Output Node to properly conclude execution. Additionally, every AI Task node should be connected to an output node to handle failures.

​
Configuration

​
Outcome Labels

Outcome Labels define the possible states your execution can end in. They work like exit codes or status tags that answer: “How did this run finish?” By default, we return success or failure, but you can define custom Outcome Labels that reflect your real business logic.

​
Common Patterns

  • Default: success, failure
  • Business Logic States: item_purchased, out_of_stock, login_required
  • Decision Outcomes: approved, rejected, needs_review

​
Example Outcome Labels

For an e-commerce checkout agent, you might define:
  • purchase_completed
  • payment_failed
  • item_unavailable
  • session_expired

​
Best Practices for Outcome Labels

  • Use clear, descriptive names (e.g., payment_completed instead of done)
  • Define all realistic end states your agent might encounter
  • Keep naming consistent across similar workflows
  • Use snake_case

​
Result Schema

Alongside an Outcome Label, an Output Node can return structured data using a Result Schema. This is a custom JSON Schema that defines the shape of the data you expect when this Output Node is reached. Using Result Schemas ensures outputs are predictable and machine-readable, and makes it easy to consume execution results via API or SDKs. Our schema format follows OpenAI Structured Outputs specification (a subset of JSON Schema).

​
When to Use a Result Schema

Use a custom Result Schema when:
  • You need structured data in a specific format
  • Other services or downstream systems expect defined fields
  • You want consistent outputs across executions and environments
  • You’re extracting multiple values (prices, names, URLs, IDs, etc.)

​
Example Result Schema

A possible schema for a product-scraping agent: 
{
  "type": "object",
  "properties": {
    "product_name": {
      "type": "string",
      "description": "The name of the product"
    },
    "price": {
      "type": "number",
      "description": "The product price"
    },
    "in_stock": {
      "type": "boolean",
      "description": "Whether the product is available"
    }
  },
  "additionalProperties": false,
  "required": ["product_name", "price", "in_stock"]
}

​
Result Schema Requirements

When defining a Result Schema, follow these rules:
  • All object types must include "additionalProperties": false
  • All properties should be listed in the "required" array
  • Use the "description" field to guide the AI in populating values correctly

​
Tips

  • Start Simple: Begin with Outcome Labels only. Add a Result Schema once you know what data you need to return.
  • Plan End States First: Think through all the ways your agent might finish (success, partial success, failures, edge cases) and design Outcome Labels accordingly.
  • Test Outcomes: Run test executions to verify the workflow can reach each defined Outcome Label.
  • Validate Your Schema: Use the examples in the docs as a reference and ensure your JSON Schema is valid before relying on it in production.