# Backend Architecture for Agent Workflow System

## Overview

This document describes the scalable, modular, and extensible backend architecture for the agent workflow system, designed to work like Zapier but optimized for AI agent workflows.

## Architecture Principles

1. **Scalability**: Horizontal scaling through microservices and queue-based processing
2. **Modularity**: Clear separation of concerns with pluggable components
3. **Extensibility**: Easy to add new triggers, nodes, and integrations
4. **Reliability**: Fault tolerance, retries, and graceful degradation
5. **Performance**: Async processing, caching, and efficient resource usage

## System Components

### 1. API Gateway Layer

- **Load Balancer**: Distributes traffic across API instances
- **Authentication**: JWT-based auth with role-based access control
- **Rate Limiting**: Per-user and per-IP rate limits
- **Request Routing**: Routes to appropriate services

### 2. Trigger System

#### Supported Trigger Types:

- **Webhook Triggers**
  - Unique URLs per workflow
  - HMAC signature validation
  - Custom header validation
  - Request/response transformation

- **Schedule Triggers**
  - Cron-based scheduling
  - Timezone support
  - Execution windows
  - Missed execution handling

- **Event Triggers**
  - Real-time event bus (Redis Pub/Sub)
  - Event filtering and routing
  - Event replay capability

- **Polling Triggers**
  - Configurable intervals
  - Change detection
  - Rate limiting

- **Manual Triggers**
  - UI-based execution
  - API-based execution
  - Bulk execution support

### 3. Workflow Engine

#### Core Components:

- **Workflow Orchestrator**
  - Manages workflow lifecycle
  - Handles execution flow
  - Manages dependencies
  - Error handling and retries

- **Workflow Executor**
  - Executes individual nodes
  - Manages parallel execution
  - Resource allocation
  - Performance monitoring

- **State Manager**
  - Distributed state management (Redis)
  - Execution context persistence
  - Checkpoint and recovery
  - Real-time status updates

### 4. Node Types

- **Agent Nodes**: AI-powered processing with multiple models
- **Tool Nodes**: Integration with external services
- **Transform Nodes**: Data manipulation and formatting
- **Condition Nodes**: If/else and switch logic
- **Loop Nodes**: For/while iterations
- **Parallel Nodes**: Concurrent execution branches
- **Webhook Nodes**: HTTP requests to external services
- **Delay Nodes**: Time-based delays

### 5. Data Flow

```
Trigger → Queue → Orchestrator → Executor → Node → Output
                       ↓              ↓         ↓
                  State Manager   Tool Service  Results
```

### 6. Storage Architecture

- **PostgreSQL**: Workflow definitions, configurations, audit logs
- **Redis**: Execution state, queues, caching, pub/sub
- **S3/Blob Storage**: Large files, logs, execution artifacts
- **TimescaleDB**: Time-series data, metrics, analytics

### 7. Queue System

- **RabbitMQ**: Task queuing, priority queues, dead letter queues
- **Kafka**: Event streaming, audit trail, real-time analytics

## Execution Flow

### 1. Trigger Phase
```python
1. Trigger fires (webhook/schedule/event/etc)
2. Validate trigger configuration
3. Create ExecutionContext
4. Queue workflow for execution
```

### 2. Orchestration Phase
```python
1. Load workflow definition
2. Build execution graph
3. Determine execution order
4. Initialize state management
```

### 3. Execution Phase
```python
1. Execute nodes in topological order
2. Handle parallel branches
3. Manage data flow between nodes
4. Update execution state
```

### 4. Completion Phase
```python
1. Aggregate results
2. Execute post-processing
3. Trigger downstream workflows
4. Clean up resources
```

## Scalability Features

### Horizontal Scaling
- Stateless API servers
- Distributed queue workers
- Shared state via Redis
- Database read replicas

### Performance Optimization
- Connection pooling
- Result caching
- Batch processing
- Async I/O throughout

### Resource Management
- Worker pool management
- Memory limits per execution
- CPU throttling
- Concurrent execution limits

## Security

### Authentication & Authorization
- JWT tokens with refresh
- API key authentication
- OAuth2 integration
- Role-based permissions

### Data Security
- Encryption at rest
- TLS for all communications
- Secret management (Vault)
- Audit logging

### Webhook Security
- HMAC signature validation
- IP whitelisting
- Rate limiting
- Request size limits

## Monitoring & Observability

### Metrics
- Prometheus metrics
- Custom business metrics
- Performance tracking
- Resource utilization

### Logging
- Structured logging
- Centralized log aggregation
- Log levels and filtering
- Correlation IDs

### Tracing
- Distributed tracing (OpenTelemetry)
- LLM monitoring (Langfuse)
- Execution visualization
- Performance profiling

### Alerting
- Error rate monitoring
- SLA tracking
- Resource alerts
- Custom alerts

## Error Handling

### Retry Strategies
- Exponential backoff
- Circuit breakers
- Dead letter queues
- Manual retry options

### Failure Modes
- Node-level failures
- Workflow-level failures
- System-level failures
- Graceful degradation

## API Endpoints

### Workflow Management
```
POST   /api/workflows                 # Create workflow
GET    /api/workflows/:id            # Get workflow
PUT    /api/workflows/:id            # Update workflow
DELETE /api/workflows/:id            # Delete workflow
POST   /api/workflows/:id/activate   # Activate workflow
POST   /api/workflows/:id/pause      # Pause workflow
```

### Execution Management
```
POST   /api/workflows/:id/execute    # Manual execution
GET    /api/executions/:id           # Get execution status
POST   /api/executions/:id/cancel    # Cancel execution
GET    /api/executions/:id/logs      # Get execution logs
```

### Trigger Management
```
GET    /api/workflows/:id/triggers   # List triggers
POST   /api/workflows/:id/triggers   # Add trigger
PUT    /api/triggers/:id             # Update trigger
DELETE /api/triggers/:id             # Remove trigger
```

### Webhook Endpoints
```
POST   /webhooks/:path               # Webhook receiver
GET    /api/webhooks                 # List webhooks
```

## Database Schema

### Core Tables

```sql
-- Workflows table
CREATE TABLE workflows (
    id UUID PRIMARY KEY,
    name VARCHAR(255),
    description TEXT,
    project_id UUID,
    status VARCHAR(50),
    definition JSONB,
    created_at TIMESTAMP,
    updated_at TIMESTAMP
);

-- Workflow executions
CREATE TABLE workflow_executions (
    id UUID PRIMARY KEY,
    workflow_id UUID,
    status VARCHAR(50),
    started_at TIMESTAMP,
    completed_at TIMESTAMP,
    context JSONB,
    result JSONB,
    error TEXT
);

-- Triggers
CREATE TABLE triggers (
    id UUID PRIMARY KEY,
    workflow_id UUID,
    type VARCHAR(50),
    config JSONB,
    is_active BOOLEAN
);

-- Webhook registrations
CREATE TABLE webhook_registrations (
    id UUID PRIMARY KEY,
    workflow_id UUID,
    path VARCHAR(255) UNIQUE,
    secret VARCHAR(255),
    config JSONB
);
```

## Deployment

### Docker Compose (Development)
```yaml
services:
  api:
    build: .
    ports:
      - "8000:8000"
    depends_on:
      - postgres
      - redis
      - rabbitmq
      
  worker:
    build: .
    command: python -m workflow_engine.worker
    depends_on:
      - postgres
      - redis
      - rabbitmq
      
  scheduler:
    build: .
    command: python -m workflow_engine.scheduler
    depends_on:
      - postgres
      - redis
```

### Kubernetes (Production)
- Deployment manifests for each service
- Horizontal Pod Autoscaling
- Service mesh (Istio)
- Persistent volume claims

## Future Enhancements

1. **Workflow Versioning**: Track and manage workflow versions
2. **A/B Testing**: Test different workflow variations
3. **Workflow Templates**: Pre-built workflow templates
4. **Advanced Analytics**: Detailed execution analytics
5. **Multi-tenancy**: Full isolation between projects
6. **Workflow Marketplace**: Share and monetize workflows
7. **Visual Debugging**: Step-through debugging
8. **Performance Optimization**: ML-based optimization