Integration Architecture

Comprehensive guide to Sindhan's integration capabilities, API frameworks, connector ecosystem, and enterprise system connectivity.

Overview

Sindhan's integration architecture enables seamless connectivity with existing enterprise systems, applications, and data sources. Built on API-first principles, the platform provides flexible integration patterns that support various deployment scenarios while maintaining security, scalability, and reliability.

Integration Framework

The Integration Flow

Every Sindhan agent follows a sophisticated integration flow that seamlessly coordinates all seven core capabilities:

Real-World Integration Example

Scenario: A Sindhan Discover agent analyzing customer churn patterns through API integration

Identity: Agent authenticates with customer data systems using its unique identity
Environment Awareness: Checks data privacy policies and regulatory constraints
Purpose: Focuses on churn prediction goals and success metrics
Memory: Recalls previous churn analysis patterns and learned insights
Advanced Context: Combines customer data, market trends, and collaborative insights
Reasoning: Analyzes patterns using integrated knowledge and context
Action: Generates churn risk scores and recommendations via API
Observability: Tracks prediction accuracy and API performance
Learning: Updates memory with new patterns and improves future predictions

Integration Architecture Components

API Architecture

RESTful API Framework

Core API Principles:

Resource-Based URLs: Intuitive endpoint structure
HTTP Method Semantics: Proper use of GET, POST, PUT, DELETE
Stateless Operations: No server-side session state
Standard Response Codes: Consistent HTTP status code usage

API Structure:

API Endpoints Overview:

Agent Management APIs:

GET    /api/v1/agents                    # List all agents
POST   /api/v1/agents                    # Create new agent
GET    /api/v1/agents/{agentId}          # Get agent details
PUT    /api/v1/agents/{agentId}          # Update agent
DELETE /api/v1/agents/{agentId}          # Delete agent
POST   /api/v1/agents/{agentId}/execute  # Execute agent task

Discovery Agent APIs:

POST   /api/v1/discovery/analyze         # Start analysis task
GET    /api/v1/discovery/results/{jobId} # Get analysis results
POST   /api/v1/discovery/predict         # Prediction request
GET    /api/v1/discovery/patterns        # Pattern discovery

Smart Operator APIs:

POST   /api/v1/operators/decide          # Decision request
POST   /api/v1/operators/execute         # Execution request
GET    /api/v1/operators/workflows       # List workflows
POST   /api/v1/operators/optimize        # Optimization request

GraphQL Interface

Schema-First Development:

type Agent {
  id: ID!
  name: String!
  type: AgentType!
  status: AgentStatus!
  capabilities: [Capability!]!
  performance: PerformanceMetrics
  created: DateTime!
  lastActive: DateTime
}
 
type Query {
  agents(filter: AgentFilter): [Agent!]!
  agent(id: ID!): Agent
  tasks(agentId: ID!): [Task!]!
  insights(timeRange: TimeRange!): [Insight!]!
}
 
type Mutation {
  createAgent(input: CreateAgentInput!): Agent!
  executeTask(input: TaskInput!): TaskResult!
  updateAgent(id: ID!, input: UpdateAgentInput!): Agent!
}
 
type Subscription {
  agentStatusUpdates(agentId: ID!): AgentStatus!
  taskProgress(taskId: ID!): TaskProgress!
  realTimeInsights: Insight!
}

Benefits of GraphQL:

Flexible Queries: Clients request exactly what they need
Real-time Subscriptions: Live updates for agent status and results
Type Safety: Strong typing prevents integration errors
Introspection: Self-documenting API schema

Webhook Framework

Event-Driven Integration:

Webhook Event Types:

Agent Events: Creation, updates, deletion, status changes
Task Events: Started, completed, failed, progress updates
Performance Events: SLA breaches, performance thresholds
Security Events: Authentication failures, suspicious activity

Connector Ecosystem

Pre-built Connectors

Enterprise Systems:

Database Connectors:

Relational: PostgreSQL, MySQL, SQL Server, Oracle
NoSQL: MongoDB, Cassandra, DynamoDB, Redis
Data Warehouses: Snowflake, BigQuery, Redshift, Databricks
Vector Databases: Pinecone, Weaviate, Chroma, Qdrant

Communication Connectors:

Email: SMTP, Exchange, Gmail API
Messaging: Slack, Microsoft Teams, Discord
Voice: Twilio, Amazon Connect, Asterisk
SMS: Twilio, AWS SNS, MessageBird

Custom Connector Development

Connector SDK Framework:

from sindhan.connectors import BaseConnector, connector_config
 
@connector_config(
    name="CustomSystem",
    version="1.0.0",
    auth_types=["api_key", "oauth2"]
)
class CustomSystemConnector(BaseConnector):
    def __init__(self, config):
        super().__init__(config)
        self.client = self._initialize_client()
    
    def test_connection(self):
        """Test connectivity to the external system"""
        return self.client.health_check()
    
    def fetch_data(self, query):
        """Fetch data from external system"""
        response = self.client.query(query)
        return self._transform_response(response)
    
    def send_data(self, data):
        """Send data to external system"""
        transformed = self._transform_data(data)
        return self.client.create(transformed)
    
    def _initialize_client(self):
        """Initialize API client with authentication"""
        # Implementation specific to external system
        pass

Connector Lifecycle:

Development: SDK-based connector creation
Testing: Automated validation and testing
Certification: Security and performance validation
Deployment: Marketplace publication
Monitoring: Performance and reliability tracking

Message Architecture

Asynchronous Messaging

Event Streaming Platform:

Message Patterns:

Publish/Subscribe: Event broadcasting to multiple consumers
Point-to-Point: Direct message delivery between systems
Request/Reply: Asynchronous request-response patterns
Message Queuing: Reliable message delivery with persistence

Synchronous Communication

Request/Response Patterns:

HTTP/REST: Standard web API communication
gRPC: High-performance RPC framework
WebSocket: Real-time bidirectional communication
Server-Sent Events: Real-time server-to-client streaming

Batch Processing

ETL Pipeline Architecture:

Integration Security

Authentication Mechanisms

OAuth 2.0 / OIDC Flow:

Authentication Types:

API Keys: Simple key-based authentication
JWT Tokens: Stateless token-based authentication
mTLS: Mutual TLS certificate authentication
SAML: Enterprise single sign-on integration

Data Encryption

End-to-End Encryption:

Transport Security: TLS 1.3 for all communications
Message Encryption: AES-256 for sensitive payloads
Key Management: Rotating encryption keys
Certificate Management: Automated certificate lifecycle

API Governance

Rate Limiting and Throttling:

Integration Patterns

Enterprise Integration Patterns

Message Routing:

Content-Based Router: Route based on message content
Recipient List: Send to multiple predetermined recipients
Splitter: Break composite messages into individual messages
Aggregator: Combine related messages into single message

Message Transformation:

Message Translator: Convert between data formats
Envelope Wrapper: Add routing information to messages
Content Filter: Remove unneeded data from messages
Content Enricher: Add information to messages

Endpoint Patterns:

Messaging Gateway: Encapsulate messaging system access
Service Activator: Connect messaging system to service
Transactional Client: Manage transactions across message operations
Competing Consumers: Multiple consumers for load distribution

Real-Time Integration

Streaming Data Integration:

Monitoring and Observability

Integration Monitoring

Metrics and KPIs:

Throughput: Messages/requests per second
Latency: End-to-end processing time
Error Rate: Failed integration attempts
Availability: System uptime and reliability

Distributed Tracing:

Request Correlation: Track requests across services
Performance Analysis: Identify bottlenecks
Error Analysis: Root cause investigation
Dependency Mapping: Understand service relationships

Health Checks and Alerting

System Health Monitoring:

Best Practices

Integration Design Principles

Loose Coupling: Minimize dependencies between systems
Idempotency: Ensure operations can be safely retried
Circuit Breaker: Prevent cascade failures
Bulkhead Pattern: Isolate critical resources
Timeout Handling: Prevent hanging operations

Performance Optimization

Connection Pooling: Reuse database and HTTP connections
Caching: Cache frequently accessed data
Compression: Reduce payload sizes
Batching: Group operations for efficiency
Async Processing: Use asynchronous patterns for scalability

Error Handling

Retry Logic: Implement exponential backoff
Dead Letter Queues: Handle failed messages
Circuit Breakers: Prevent system overload
Graceful Degradation: Maintain partial functionality
Comprehensive Logging: Enable troubleshooting

Need technical support for integration architecture? Contact: integrations@sindhan.ai

Security Architecture Overview