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Combine the reasoning capabilities of agents with the structured automation of flows.

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Agent as Flow Node

Use agents within flows for intelligent processing: Use Cases:
  • Add reasoning to structured workflows
  • Dynamic decision making within flows
  • Context-aware processing
  • Multi-step analysis
Example: 
Customer Request
  → Extract Data
  → Agent (analyze request, determine best solution)
  → Execute Solution Flow
  → Send Response

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Common Patterns

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Agent-Driven Workflow Selection

Agent chooses which workflow to execute: Use Case: Intelligent routing based on request analysis Configuration:
  • Agent has access to multiple flows as tools
  • Instructions guide flow selection
  • Agent explains reasoning
  • Results returned to user

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Multi-Agent Orchestration

Coordinate multiple specialized agents: Use Case: Break complex tasks into specialized subtasks Configuration:
  • Main orchestrator agent with delegation tools
  • Specialized agents for specific domains
  • Result aggregation logic
  • Quality control steps

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Agent with Human Review

Agent proposes action, human approves: Use Case: High-stakes decisions requiring human approval Configuration:
  • Agent generates proposal
  • Human-in-the-loop node
  • Approval workflow
  • Revision loop

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Iterative Refinement

Agent iterates until quality threshold met: Use Case: Content generation with quality control Configuration:
  • Quality evaluation criteria
  • Maximum iterations
  • Feedback generation
  • Acceptance threshold

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Flow Provides Tools to Agent

Flows as tools that agents can execute:

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Pattern: Agent Orchestrator

Use Case: Agent uses flows as tools to accomplish tasks Configuration:
  • Agent instructions mention available flows
  • Flows configured as agent tools
  • Agent decides when to use each flow
  • Results integrated into conversation

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Pattern: Agentic Workflow

Use Case: Agent plans and executes multi-step workflow Configuration:
  • Agent has planning instructions
  • Multiple flows available as tools
  • Agent evaluates results between steps
  • Adaptive execution based on outcomes

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Advanced Scenarios

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Research and Report Generation

Use Case: Comprehensive research reports with multiple sources

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Customer Support Automation

Use Case: Automated first-line support with escalation

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Data Processing Pipeline

Use Case: Intelligent data processing with validation

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Best Practices

Agent-Flow Integration:
  • Give agents clear instructions about available flows
  • Name flows descriptively
  • Document flow inputs/outputs
  • Test flows independently before giving to agents
Error Handling:
  • Handle flow failures in agent logic
  • Provide fallback options
  • Log errors for debugging
  • Notify humans when needed
Performance:
  • Use flows for heavy processing
  • Use agents for decision making
  • Cache expensive operations
  • Monitor execution times
Cost Optimization:
  • Use appropriate models for agents
  • Minimize agent iterations
  • Leverage flows for deterministic tasks
  • Monitor token usage

Working with AI

AI nodes and agents guide

Working with Documents

Document processing scenarios