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Leverage AI nodes and agents to build intelligent, adaptive automation.

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AI Nodes

AI nodes provide specific AI capabilities within flows:

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Text Generation

Generate content, summaries, and responses: Use Cases:
  • Content creation
  • Email drafting
  • Report generation
  • Text summarization
Example: 
Input: Product description
  → Generate Text (create marketing copy)
  → Output: Compelling product description

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Structured Outputs

Extract structured data from text: Use Cases:
  • Form filling
  • Data extraction
  • Classification
  • Entity recognition
Example: 
Input: Customer email
  → Generate Fields (extract: name, email, issue, priority)
  → Output: Structured ticket data

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Vision & Multimodal

Process images and visual content: Use Cases:
  • Image analysis
  • OCR and text extraction
  • Chart interpretation
  • Visual QA
Example: 
Input: Invoice image
  → Interpret Image (extract line items)
  → Output: Structured invoice data

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Categorization

Classify content into categories: Use Cases:
  • Content routing
  • Sentiment analysis
  • Priority assignment
  • Topic classification
Example: 
Input: Support ticket
  → Categorizer (classify: Technical, Billing, General)
  → Route to appropriate team

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Agents in Flows

Use agents as nodes within flows for intelligent processing:

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Agent Node

Execute agent reasoning within a flow: Use Cases:
  • Multi-step reasoning
  • Dynamic decision making
  • Tool orchestration
  • Context-aware processing
Example: 
Customer Request
  → Agent (analyze request, gather info, determine solution)
  → Execute Solution Flow
  → Return Result
Configuration:
  • Agent instructions
  • Available tools
  • Model selection
  • Timeout settings

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When to Use Agents vs AI Nodes

  • Single, specific AI operation
  • Structured, predictable task
  • No reasoning required
  • Fast execution needed
  • Cost-sensitive

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Common Patterns

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Content Generation Pipeline

Use Case: Automated content creation with research and generation

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Intelligent Data Processing

Use Case: Process unstructured data with AI understanding

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Multi-Agent Collaboration

Use Case: Orchestrate multiple specialized agents for complex tasks

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Model Selection

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By Task Complexity

Simple Tasks (GPT-3.5, Mistral):
  • Classification
  • Simple extraction
  • Template filling
  • Basic summarization
Moderate Tasks (GPT-4o, Claude Sonnet):
  • Content generation
  • Analysis
  • Multi-step reasoning
  • General-purpose agents
Complex Tasks (GPT-4, Claude Opus):
  • Deep reasoning
  • Creative problem solving
  • Complex analysis
  • Critical decisions

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By Cost/Performance

High Volume, Low Complexity:
  • Use faster, cheaper models
  • Batch processing
  • Simple prompts
  • Minimal context
Low Volume, High Complexity:
  • Use premium models
  • Rich context
  • Detailed instructions
  • Multiple iterations

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Best Practices

Prompt Engineering:
  • Be specific and clear
  • Provide examples
  • Set output format
  • Include constraints
Model Selection:
  • Match model to task complexity
  • Test different models
  • Monitor quality vs cost
  • Optimize based on results
Error Handling:
  • Validate AI outputs
  • Implement fallbacks
  • Log failures
  • Retry with adjustments
Cost Management:
  • Use appropriate models
  • Set token limits
  • Cache results
  • Monitor spending

Working with Documents

Document processing scenarios

Agents in Flows

Using agents within flows