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Workflow Builder

Design custom conversation journeys for your AI agents using our intuitive drag-and-drop workflow builder. Create sophisticated chat flows with conditional logic, data collection, and automated actions.

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Getting Started

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Creating a Workflow

1. Switch to Workflow Mode: Click the “Workflow” button in your agent configuration
2. Access Workflow Builder: Navigate to the “Workflow Builder” tab
3. Create New Workflow: Click ”+ Create Workflow” to start building
4. Name Your Workflow: Provide a descriptive name and optional description

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Workflow Management

Once created, you can manage your workflows with the following actions:

• Open Builder: Access the visual workflow editor
• Edit: Modify workflow name and description
• Delete: Remove the workflow permanently
• Status: Track workflow status (Draft, Published)

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Workflow Builder Interface

The workflow builder provides a comprehensive visual interface for designing conversation flows:

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Canvas Area

• Drag-and-Drop Interface: Build workflows by dragging nodes from the sidebar
• Visual Flow Design: Connect nodes to create conversation paths
• Real-time Preview: See your workflow structure as you build

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Node Sidebar

Access all available node types for building your workflow:

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Properties Panel

Configure each node’s settings and behavior:

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Node Types

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Landing Page

Purpose: Create the initial interaction point for users entering your workflow Configuration:

• Node Name: Identify the node in your workflow
• Description: Optional description for documentation
• Heading: Welcome message or title for users
• Content: Detailed text displayed below the heading

Use Cases:

• Welcome messages for new visitors
• Introduction to services or products
• Setting expectations for the conversation

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Message

Purpose: Deliver static content or information to users at specific points in the conversation Configuration:

• Message Content: The text to be sent to the user
• Formatting: Support for rich text and basic formatting
• Delay Settings: Optional delay before sending the message

Use Cases:

• Providing information or instructions
• Confirming user actions
• Delivering notifications or updates

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LLM (Large Language Model)

Purpose: Integrate AI-powered responses and intelligent conversation handling Configuration:

• Model Selection: Choose the AI model to use
• System Prompt: Define the AI’s behavior and personality
• Context Management: Control conversation history and context
• Response Parameters: Temperature, max tokens, and other AI settings

Use Cases:

• Dynamic question answering
• Intelligent conversation routing
• Content generation and assistance

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Condition

Purpose: Create decision points that route conversations based on user input, data, or other criteria Configuration:

• Condition Logic: Define the criteria for branching
• Multiple Paths: Create different routes based on conditions
• Default Path: Fallback route when conditions aren’t met
• Variable Evaluation: Use collected data in conditions

Use Cases:

• Routing based on user preferences
• Handling different user types or roles
• Creating complex conversation logic

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Form

Purpose: Gather specific information from users through structured input fields Configuration:

• Field Types: Text, email, number, select, etc.
• Validation Rules: Required fields and format validation
• Field Labels: User-friendly labels for each input
• Submission Handling: Define what happens after form completion

Use Cases:

• User registration and onboarding
• Contact information collection
• Survey and feedback forms
• Service request details

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Human Agent

Purpose: Transfer the conversation to a live human agent when AI assistance isn’t sufficient Configuration:

• Agent Assignment: Specify which agents can handle transfers
• Transfer Conditions: Define when transfers should occur
• Context Passing: Share conversation history with human agents
• Escalation Rules: Set priority and urgency levels

Use Cases:

• Complex issue resolution
• Sales conversations requiring human touch
• Sensitive customer complaints
• Technical support escalation

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End

Purpose: Properly conclude the workflow and end the conversation Configuration:

• Ending Message: Final message to the user
• Session Cleanup: Clear temporary data and variables
• Analytics Tracking: Record completion metrics
• Follow-up Actions: Optional post-conversation tasks

Use Cases:

• Successful conversation completion
• User-initiated conversation ending
• Error handling and graceful exits
• Redirecting to external resources

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Workflow Management

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Workflow States

Draft:

• Workflow is being created or edited
• Not active for live conversations
• Changes can be made freely

Published:

• Workflow is live and handling conversations
• Users interact with the published version
• Editing requires creating a new draft

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Best Practices

1. Start Simple: Begin with basic flows and add complexity gradually
2. Test Thoroughly: Use the preview feature to test all conversation paths
3. Plan User Journeys: Map out expected user interactions before building
4. Use Clear Naming: Give nodes descriptive names for easy maintenance
5. Handle Edge Cases: Include fallback paths for unexpected user behavior
6. Regular Updates: Keep workflows current with business changes

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Advanced Features

Variable Management:

• Store and use data collected throughout the conversation
• Pass information between nodes
• Use variables in conditions and messages

Integration Capabilities:

• Connect to external APIs and services
• Trigger webhooks and notifications
• Database operations and data storage

Analytics and Monitoring:

• Track user progress through workflows
• Identify bottlenecks and drop-off points
• Measure conversion rates and success metrics

AI Customization

Back: Learn about AI agent configuration and customization