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What is a Workflow

A workflow in Vectense Platform is an intelligent automation that processes information, makes decisions, and executes actions to handle your business tasks automatically.

Core Concepts​

Workflow Definition​

A workflow is a structured process that:

  • Starts when something happens (scheduled time, new email, file change)
  • Processes information through a series of steps
  • Uses AI models and knowledge bases for intelligent decisions
  • Executes actions like sending emails, updating files, or creating reports
  • Tracks execution and shows you what happened

Key Components​

Triggers Events that start your workflow:

  • Time-based schedules (daily, hourly, specific times)
  • External notifications from other systems
  • File changes (new files, modifications)
  • Email arrivals and database updates

Steps Sequential actions that process information:

  • AI model interactions for content analysis
  • Requests to external web services
  • File read/write operations
  • Information transformations and checks
  • Decision making and branching

Memory System Information storage and flow between steps:

  • Variables that store intermediate results
  • Context preservation throughout execution
  • Safe information handling
  • Organized data access and management

Workflow Architecture​

Execution Flow​

Trigger → Step 1 → Step 2 → Step N → Completion
    ↓         ↓         ↓         ↓
  Memory ← Memory ← Memory ← Memory
  1. Trigger Activation: An event starts the workflow
  2. Step Execution: Each step processes data sequentially
  3. Memory Management: Data flows between steps via memory variables
  4. Completion: Workflow finishes with success or error status

Information Flow​

  • Input Information: Comes from triggers (external notifications, file content, scheduled events)
  • Intermediate Information: Generated by workflow steps and stored in memory
  • Output Information: Final results sent to external systems or stored as files

AI Integration​

  • Model Selection: Choose appropriate AI models for each step
  • Context Injection: Provide relevant knowledge base content to AI
  • Prompt Engineering: Craft effective prompts for desired outcomes
  • Response Processing: Parse and use AI model outputs

Workflow Types​

Event-Driven Workflows​

Respond to external events in real-time:

  • Customer Support: Process incoming support tickets
  • Order Processing: Handle new orders from e-commerce systems
  • Alert Management: Respond to system alerts and notifications
  • Content Moderation: Review user-generated content automatically

Scheduled Workflows​

Run on predetermined schedules:

  • Report Generation: Create daily, weekly, or monthly reports
  • Data Synchronization: Keep systems in sync on regular intervals
  • Maintenance Tasks: Perform routine system maintenance
  • Batch Processing: Process accumulated data in batches

Data Processing Workflows​

Transform and analyze data:

  • Document Analysis: Extract information from documents
  • Content Classification: Categorize content automatically
  • Data Validation: Check data quality and completeness
  • Information Extraction: Pull specific data from unstructured sources

Workflow States​

Active Workflows​

Workflows that are enabled and can be triggered:

  • Trigger monitoring is active
  • Ready to execute when events occur
  • Consuming system resources for monitoring
  • Visible in active workflow lists

Inactive Workflows​

Workflows that are disabled:

  • No trigger monitoring
  • Cannot be executed automatically
  • Can still be run manually for testing
  • Useful for development and testing phases

Workflow Execution States​

Individual workflow runs have states:

  • Queued: Waiting to start execution
  • Running: Currently executing steps
  • Completed: Successfully finished all steps
  • Failed: Encountered an error during execution

Persona System​

What is a Persona​

A persona defines the AI agent's behavior, role, and communication style within workflows. It includes:

  • Role: The agent's function (analyst, assistant, reviewer)
  • Goals: What the agent is trying to achieve
  • Skills: Specific capabilities and expertise areas
  • Communication Style: How the agent should express responses
  • Guardrails: Boundaries and limitations for agent behavior

Persona Configuration​

Role Definition

  • Clearly define the agent's purpose and responsibilities
  • Examples: "Content Analyst", "Customer Service Representative", "Data Validator"

Goal Setting

  • Specify what the agent should accomplish
  • Examples: "Extract key information accurately", "Provide helpful customer responses"

Skills Assignment

  • List specific capabilities the agent should have
  • Examples: "Document analysis", "Customer communication", "Data validation"

Communication Style

  • Define how the agent should respond
  • Examples: "Professional and concise", "Friendly and helpful", "Technical and detailed"

Guardrails

  • Set boundaries for agent behavior
  • Examples: "Never share personal information", "Always maintain professional tone"

Memory and Context​

Memory Variables​

Workflows use memory variables to store and share data:

  • Input Variables: Data received from triggers
  • Processing Variables: Intermediate results from workflow steps
  • Output Variables: Final results to be used by actions

Variable Types​

  • String: Text data like messages, names, descriptions
  • Number: Numeric data for calculations and counts
  • Object: Complex data structures like JSON responses
  • Array: Lists of items for batch processing
  • Boolean: True/false values for conditional logic

Context Management​

  • Step Context: Data available within a single step
  • Workflow Context: Data shared across all steps in a workflow
  • Global Context: Data that persists across workflow executions

Error Handling​

Error Types​

Configuration Errors

  • Invalid trigger settings
  • Missing required parameters
  • Incorrect integration configurations

Runtime Errors

  • API call failures
  • File access problems
  • AI model connectivity issues

Data Errors

  • Invalid input data formats
  • Missing required data fields
  • Data validation failures

Error Recovery​

  • Retry Logic: Automatically retry failed steps
  • Fallback Actions: Alternative actions when primary actions fail
  • Error Notifications: Alert administrators of critical failures
  • Graceful Degradation: Continue execution with reduced functionality

Performance and Monitoring​

Execution Metrics​

  • Duration: Time taken to complete workflows
  • Success Rate: Percentage of successful executions
  • Error Rate: Frequency of workflow failures
  • Resource Usage: AI model tokens and processing time

Cost Tracking​

  • AI Model Costs: Token usage and associated costs
  • Processing Costs: Compute resources used
  • Storage Costs: Data storage requirements
  • Total Cost per Execution: Complete cost breakdown

Optimization Opportunities​

  • Workflow Design: Streamline unnecessary steps
  • AI Model Selection: Choose appropriate models for tasks
  • Parallel Processing: Execute independent steps simultaneously
  • Caching: Reuse results when appropriate

Security and Access Control​

Workflow Permissions​

  • Create: Who can create new workflows
  • Edit: Who can modify existing workflows
  • Execute: Who can run workflows manually
  • View: Who can see workflow configurations and results

Data Security​

  • Encryption: Data encrypted at rest and in transit
  • Access Controls: Role-based access to sensitive data
  • Audit Logging: Complete tracking of workflow activities
  • Data Isolation: Workspace-level data separation

Integration Security​

  • API Key Management: Secure storage of integration credentials
  • Network Security: Secure communication with external systems
  • Input Validation: Sanitize all external input data
  • Output Filtering: Control what data is shared externally

Best Practices​

Workflow Design​

  • Single Responsibility: Each workflow should have a clear, focused purpose
  • Modularity: Break complex processes into smaller, manageable workflows
  • Error Handling: Always include error handling and recovery logic
  • Documentation: Add clear descriptions and comments

Performance Optimization​

  • Efficient AI Usage: Use appropriate models for each task
  • Data Management: Minimize data transfer and storage
  • Parallel Processing: Execute independent tasks simultaneously
  • Resource Monitoring: Track and optimize resource usage

Maintenance​

  • Regular Testing: Test workflows with various input scenarios
  • Performance Monitoring: Track execution metrics over time
  • Version Control: Maintain workflow versions and changes
  • Documentation Updates: Keep documentation current with changes

Now that you understand workflow fundamentals, proceed to Create a New Workflow to build your first automation.