AI Workflow Architecture

This document describes the architecture and design decisions for AI Workflow.

System Overview

AI Workflow is designed to support multiple independent workflows, each with its own execution context, configuration, and monitoring. The system provides a unified platform for managing complex AI-driven processes.

Architecture Diagram

graph TB
    subgraph "Client Layer"
        A[Web Client]
        B[CLI Client]
        C[API Client]
    end
    
    subgraph "Application Layer"
        D[API Gateway]
        E[Workflow Manager]
        F[Workflow Registry]
        G[Execution Engine]
    end
    
    subgraph "Workflow Layer"
        H[Workflow 1]
        I[Workflow 2]
        J[Workflow 3]
        K[Workflow N...]
    end
    
    subgraph "AI Services Layer"
        L[OpenAI]
        M[Anthropic]
        N[Custom AI Services]
    end
    
    subgraph "Data Layer"
        O[Workflow Database]
        P[Execution Logs]
        Q[Analytics Store]
    end
    
    A --> D
    B --> D
    C --> D
    D --> E
    E --> F
    E --> G
    F --> H
    F --> I
    F --> J
    F --> K
    G --> H
    G --> I
    G --> J
    G --> K
    H --> L
    H --> M
    I --> L
    I --> N
    J --> M
    K --> N
    G --> O
    G --> P
    E --> Q
    
    style A fill:#e1f5ff
    style B fill:#e1f5ff
    style C fill:#e1f5ff
    style D fill:#fff4e1
    style E fill:#fff4e1
    style F fill:#fff4e1
    style G fill:#fff4e1
    style H fill:#e8f5e9
    style I fill:#e8f5e9
    style J fill:#e8f5e9
    style K fill:#e8f5e9
    style L fill:#fce4ec
    style M fill:#fce4ec
    style N fill:#fce4ec
    style O fill:#f3e5f5
    style P fill:#f3e5f5
    style Q fill:#f3e5f5

Core Components

Component 1: Workflow Manager

Purpose: Central orchestration and management of all workflows

Responsibilities:

• Workflow creation and configuration
• Workflow lifecycle management
• Resource allocation and scheduling
• Multi-workflow coordination

Component 2: Workflow Registry

Purpose: Storage and retrieval of workflow definitions

Responsibilities:

• Workflow metadata storage
• Version control for workflows
• Workflow discovery and search
• Template management

Component 3: Execution Engine

Purpose: Executes workflows and manages their runtime

Responsibilities:

• Step-by-step execution
• Error handling and retries
• Resource management
• Performance optimization

Component 4: Workflow Instances

Purpose: Individual workflow execution contexts

Responsibilities:

• Isolated execution environment
• State management
• Step coordination
• Result aggregation

Data Flow

sequenceDiagram
    participant Client
    participant Manager
    participant Registry
    participant Engine
    participant Workflow
    participant AIService
    
    Client->>Manager: Create Workflow
    Manager->>Registry: Store Definition
    Registry-->>Manager: Workflow ID
    Manager-->>Client: Workflow Created
    
    Client->>Manager: Execute Workflow
    Manager->>Engine: Start Execution
    Engine->>Workflow: Initialize
    Workflow->>AIService: Process Step
    AIService-->>Workflow: Result
    Workflow->>Engine: Step Complete
    Engine->>Manager: Execution Status
    Manager-->>Client: Final Result

Multi-Workflow Architecture

The system is designed to handle multiple workflows concurrently:

Workflow Isolation

Each workflow runs in its own isolated context:

• Independent state management
• Separate resource allocation
• Isolated error handling
• Individual monitoring

Workflow Coordination

Workflows can be coordinated through:

• Shared data stores
• Event-driven triggers
• Workflow dependencies
• Parallel execution

Design Decisions

Decision 1: Multi-Workflow Support

Context: Need to support multiple independent workflows simultaneously

Decision: Implement a workflow registry and manager pattern

Consequences:

• ✅ Scalable architecture
• ✅ Independent workflow execution
• ✅ Easy workflow management
• ⚠️ Additional complexity in coordination

Decision 2: Plugin-Based AI Services

Context: Support multiple AI providers and services

Decision: Use a plugin architecture for AI service integration

Consequences:

• ✅ Easy to add new AI services
• ✅ Flexible workflow configuration
• ⚠️ Requires plugin development

Scalability Considerations

• Horizontal Scaling: Workflows can be distributed across multiple execution nodes
• Vertical Scaling: Individual workflows can scale resources as needed
• Caching: Workflow definitions and results are cached for performance
• Load Balancing: Execution engine distributes workload across available resources

Security Architecture

• Authentication: API key-based authentication
• Authorization: Role-based access control for workflows
• Data Encryption: All data encrypted in transit and at rest
• Isolation: Workflows are isolated from each other

Technology Stack

Layer	Technology	Version
Frontend	React	Latest
Backend	Node.js	20.0+
Database	PostgreSQL	14+
Cache	Redis	7+
AI Services	OpenAI, Anthropic	Latest

Next Steps

• API Reference - Explore the API documentation
• Getting Started - Set up your project
• Overview - Return to overview