AI Agent Workflow Platform: Enterprise Architecture Design

A distributed, multi-tenant platform for AI-driven workflow automation with conversational DSL generation

Executive Summary

This document outlines the architecture for an enterprise-grade AI agent workflow platform that enables end users to define complex workflows through natural language conversations with AI agents. The platform generates executable DSL definitions, supports multi-tenancy at scale, and can be deployed across customer-managed Kubernetes clusters while leveraging cloud-based SDLC ecosystems.

System Overview

Core Principles

Conversational Workflow Design: AI agents collaborate with users to generate workflow DSL
Multi-Tenant by Design: Account-based isolation supporting thousands of tenants
Cloud-Agnostic Deployment: Runs on customer infrastructure (EKS, AKS, bare metal K8s)
Event-Driven Architecture: Decoupled services communicating via auto-scaling event bus
Polyglot Microservices: Multiple languages optimized per service domain
Zero-Trust Security: OIDC integration with encrypted secrets management
Avoiding Distributed Monolith: Direct service communication with clear boundaries - no service mesh complexity
Service Data Ownership: Each microservice owns its data store and cache for independent SDLC

High-Level Architecture

System Overview

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External Integration Requirements

The platform’s workflow execution engine requires integration with customer’s existing business systems and APIs to provide real-world functionality:

Customer API Dependencies

Business Operations APIs:

Kitchen Status API: Real-time kitchen capacity, equipment status, order queue information
Inventory Management API: Stock levels, ingredient availability, supply chain data
Point of Sale (POS) API: Order processing, payment status, customer information
Staff Scheduling API: Employee availability, shift schedules, labor costs
Delivery/Logistics API: Driver availability, delivery tracking, route optimization

Integration Patterns:

REST/GraphQL APIs: Standard HTTP-based integration for most business systems
Webhook Subscriptions: Real-time event notifications from customer systems
Database Connections: Direct database access for legacy systems without APIs
Message Queue Integration: Enterprise message buses (RabbitMQ, Apache Kafka)
Third-Party Connectors: Pre-built integrations for common business platforms

API Capability Requirements

For workflows to function effectively, customer APIs must provide:

Mandatory Capabilities:

Authentication: OAuth2/API key support for secure access
Rate Limiting Information: API quotas and throttling policies
Error Handling: Standardized error codes and retry mechanisms
Data Validation: Input validation and constraint checking

Recommended Capabilities:

Idempotency: Safe retry mechanisms for critical operations
Bulk Operations: Batch processing for efficiency
Real-time Updates: WebSocket or webhook support for live data
Filtering/Pagination: Efficient data retrieval for large datasets

Workflow DSL Integration Pattern

Workflow: Order Processing
├── Trigger: Customer places order
├── Step 1: Check kitchen capacity → Customer Kitchen API
├── Step 2: Verify inventory → Customer Inventory API  
├── Step 3: Schedule preparation → Customer Kitchen API
└── Result: Order confirmed or rejected

External Dependencies:
- Customer.KitchenAPI.getCapacity()
- Customer.InventoryAPI.checkStock(items)
- Customer.KitchenAPI.scheduleOrder(order, time)

Customer Onboarding Requirements

Technical Prerequisites:

API Documentation: OpenAPI/Swagger specifications for all integrated endpoints
Test Environment: Sandbox APIs for workflow development and testing
Monitoring Access: API health metrics and performance data
Security Compliance: Authentication mechanisms and data encryption standards

Business Prerequisites:

Process Documentation: Clear business process definitions and rules
Data Mapping: Field mappings between customer systems and workflow DSL
Error Scenarios: Exception handling and business rule validation
Performance SLAs: Expected response times and availability requirements

This external integration layer is critical for the platform’s value proposition - without access to customer’s business systems, workflows remain abstract and cannot drive real operational outcomes.

Detailed Platform Services Flow (Business-First Architecture)

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Event & Data Flow Architecture

Event Flow Patterns

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Multi-Tenant Event Partitioning

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Business Domain Data Models

Each business service owns its data schema, managed by dedicated database operators. Tenant data is stored in a shared, multi-tenant schema per service with strict logical isolation using a required tenant_id key and partitioning/row-level security where supported. The platform does not provision one database per tenant by default; instead, it provisions one database per service (multi-tenant) to preserve service autonomy while enabling efficient scaling and governance.

Platform Service Data Schema

Platform Service Database:
├── Account Management
│   ├── Tenant accounts and tiers
│   ├── Resource limits and quotas
│   └── Billing and subscription data
├── Authentication & Sessions
│   ├── User session tracking
│   ├── JWT token validation cache
│   └── Authorization policy cache
└── System Configuration
    ├── Tenant-specific settings
    ├── Feature flags and capabilities
    └── Runtime configuration data

Conversation Service Data Schema

Conversation Service Database:
├── Conversation Management
│   ├── Conversation metadata and hierarchy
│   ├── User-AI interaction sessions
│   └── Conversation branching and threading
├── Message Storage
│   ├── Message content and attachments
│   ├── Role-based message types (user/assistant/system)
│   └── Message versioning and edits
└── Analytics & Search
    ├── Conversation performance metrics
    ├── User engagement patterns
    └── Content search indexing

Workflow Service Data Schema

Workflow Service Database:
├── Workflow Definitions
│   ├── DSL workflow specifications
│   ├── Version management and history
│   └── Source conversation linkage
├── Execution Management
│   ├── Workflow execution instances
│   ├── Execution state and progress
│   └── Performance and timing metrics
└── Task Orchestration
    ├── Individual task executions
    ├── Task dependencies and scheduling
    └── Error handling and retry logic

Agent Service Data Schema

Agent Service Database:
├── Agent Configuration
│   ├── Agent personality and behavior settings
│   ├── LLM provider preferences
│   └── Custom agent capabilities
├── Memory Management
│   ├── Long-term conversation context
│   ├── User preference learning
│   └── Knowledge base and facts
└── Usage Analytics
    ├── LLM provider usage and costs
    ├── Performance metrics and optimization
    └── Model selection and routing data

Note: Cross-service references (like source_conversation_id in Workflow Service) are logical references only - services communicate via events, not direct database foreign keys.

Platform Deployment Configuration

Platform Usage Lifecycle

The platform supports the following workflow creation lifecycle once deployed:

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CDK8s Deployment Configuration

The platform is deployed using CDK8s templates that generate Kubernetes manifests:

Platform Deployment Structure (Phases 2–4 on Kubernetes):
├── Infrastructure Controllers
│   ├── Database Controller (PostgreSQL management)
│   ├── Cache Controller (Redis management)
│   ├── Event Bus Controller (Message routing)
│   ├── Storage Controller (Volume management)
│   ├── LLM Controller (Model serving)
│   └── Observability Controller (Monitoring)
└── Application Services
    ├── API Gateway (Request routing)
    ├── Conversation Service (AI interactions)
    ├── Workflow Service (Process orchestration)
    ├── Agent Service (AI agent management)
    └── Platform Service (System management)

Service Configuration Pattern:
Each Business Service Contains:
├── Internal Layers
│   ├── API Layer (External interface)
│   ├── Business Layer (Domain logic)
│   └── Database Layer (Data access)
├── Infrastructure Requests
│   ├── Database (Dedicated per service, multi-tenant schema with `tenant_id` partitioning/RLS)
│   ├── Cache (Dedicated per service)
│   ├── Storage (Optional file storage)
│   ├── Event Topics (Service-specific)
│   └── Specialized Needs (TimeSeries, Search, Vector DB, LLM)
└── Event Contracts
    ├── Published Events (Service publishes)
    └── Subscribed Events (Service consumes)

Inter-Service Event Contracts

Services communicate via well-defined events to maintain loose coupling:

Event Flow Summary

Conversation Service Events:
├── ConversationStarted (accountId, conversationId, userId)
└── MessageReceived (conversationId, messageId, role)

Agent Service Events:
├── DSLGenerated (conversationId, dslContent, agentId)
└── AgentResponse (conversationId, messageId, response)

Workflow Service Events:
├── WorkflowCreated (workflowId, sourceConversationId)
└── WorkflowExecutionStarted (workflowId, executionId)

Platform Service Events:
├── TenantCreated (accountId, accountName, tier)
└── QuotaExceeded (accountId, resourceType, usage)

Cross-references: Services communicate via events, not direct database foreign keys. Event Schema Standard: All events include accountId, timestamp, and service-specific identifiers. Loose Coupling: Services can evolve independently as long as event contracts are maintained.

Service Architecture Details

Business Domain Services

Services are organized by business capability first, with internal layered architecture:

API Gateway (Go/Envoy)

Business Scope:
  - Request routing to business services
  - Authentication token validation
  - Rate limiting and throttling per tenant
  - Request/response transformation

Internal Architecture:
  API Layer (gateway-api):
    - Multi-protocol support (HTTP, gRPC, WebSocket)
    - Dynamic route configuration
    - Health check aggregation
    - Circuit breaker integration
  
  Routing Layer (gateway-routing):
    - Path-based service routing
    - Load balancing across service instances
    - Tenant context extraction
    - Request correlation and tracing
  
  Data Access Layer (gateway-data):
    - Route configuration cache
    - Service discovery integration
    - Usage metrics collection

Inter-Service Communication:
  - Routes requests to all business services
  - Does not publish domain events (infrastructure concern)
  - Subscribes to service health events for routing updates

Conversation Service (Python/Go)

Business Scope:
  - Complete conversation lifecycle management
  - AI-human interaction coordination
  - Context and memory management
  - Conversation search and analytics

Internal Architecture:
  API Layer (conversation-api):
    - RESTful conversation endpoints
    - WebSocket for real-time chat
    - GraphQL for complex queries
    - Request validation and routing
  
  Business Logic Layer (conversation-logic):
    - Conversation orchestration
    - Context window management
    - Message threading and branching
    - Privacy and content filtering
  
  Data Access Layer (conversation-data):
    - PostgreSQL for conversation metadata
    - Redis for active session state
    - Object storage for message content
    - Elasticsearch for search indexing

Inter-Service Communication:
  - Publishes: ConversationStarted, MessageReceived, ConversationEnded
  - Subscribes: DSLGenerated, WorkflowCompleted, AgentResponse

Workflow Service (Java/Kotlin)

Business Scope:
  - Complete workflow lifecycle (design → execution → monitoring)
  - DSL compilation and validation
  - Task orchestration and scheduling
  - Workflow analytics and optimization

Internal Architecture:
  API Layer (workflow-api):
    - Workflow CRUD operations
    - Execution control endpoints
    - Real-time status streaming
    - Webhook management
  
  DSL Engine Layer (dsl-engine):
    - DSL parsing and validation
    - Workflow compilation
    - Dependency resolution
    - Version management
  
  Execution Engine Layer (execution-engine):
    - Task orchestration
    - State management
    - Error handling and retries
    - Resource allocation
  
  Data Access Layer (workflow-data):
    - PostgreSQL for workflow definitions
    - Redis for execution state
    - TimeSeries DB for metrics
    - Object storage for artifacts

Inter-Service Communication:
  - Publishes: WorkflowStarted, TaskCompleted, WorkflowFailed
  - Subscribes: ConversationApproved, TriggerReceived

Agent Service (Go/Python)

Business Scope:
  - AI agent orchestration and coordination
  - LLM provider management and routing
  - Agent memory and learning
  - Performance optimization

Internal Architecture:
  API Layer (agent-api):
    - Agent interaction endpoints
    - Model selection interface
    - Performance monitoring
    - Cost tracking
  
  LLM Routing Layer (llm-routing):
    - Intelligent model selection
    - Load balancing across providers
    - Cost optimization logic
    - Failover and circuit breaking
  
  Memory Layer (agent-memory):
    - Long-term conversation memory
    - User preference learning
    - Context summarization
    - Knowledge graph maintenance
  
  Data Access Layer (agent-data):
    - PostgreSQL for agent configs
    - Redis for session memory
    - Vector DB for embeddings
    - Object storage for training data

Inter-Service Communication:
  - Publishes: DSLGenerated, AgentResponse, ModelSwitched
  - Subscribes: ConversationStarted, UserFeedback

Platform Service (Go)

Business Scope:
  - Multi-tenant platform management
  - Authentication and authorization
  - Resource quotas and billing
  - System monitoring and health

Internal Architecture:
  API Layer (platform-api):
    - Tenant management endpoints
    - Authentication endpoints
    - System admin interface
    - Health check endpoints
  
  Auth Logic Layer (auth-logic):
    - OIDC integration
    - JWT token management
    - RBAC policy engine
    - Session management
  
  Tenant Routing Layer (tenant-routing):
    - Request routing by account
    - Resource quota enforcement
    - Rate limiting per tenant
    - Usage tracking
  
  Data Access Layer (platform-data):
    - PostgreSQL for tenant configs
    - Redis for session store
    - TimeSeries DB for usage metrics
    - Audit log storage

Inter-Service Communication:
  - Publishes: TenantCreated, QuotaExceeded, AuthEvent
  - Subscribes: All service events (for audit/monitoring)

Local LLM Services

Local LLM Infrastructure (Python/C++)

Components:
  - Ollama: Easy model deployment and management
  - vLLM: High-performance inference serving
  - Text Generation Inference: Hugging Face serving
  - Model Manager: Automated model lifecycle

Capabilities:
  - Multi-model serving (Llama, Mistral, CodeLlama, etc.)
  - GPU acceleration (NVIDIA, AMD)  
  - Model quantization (4-bit, 8-bit)
  - Auto-scaling based on demand
  - Model hot-swapping and A/B testing

Security:
  - All inference stays within customer infrastructure
  - No data leaves customer boundary
  - Encrypted model storage
  - Resource isolation per tenant

Note on LLM providers: Local LLM serving applies to self-hosted inference entirely within the customer boundary. Cloud LLM providers (e.g., OpenAI, Anthropic) are optional, disabled by default, and can be enabled per tenant via policy. When enabled, outbound inference requests obey data handling policies and redaction rules.

Workflow Services

DSL Compiler Service (Rust/Go)

Responsibilities:
  - DSL validation and parsing
  - Workflow topology analysis
  - Version management and migration
  - Dependency resolution
  - Code generation for execution

Features:
  - Real-time validation with detailed error messages
  - Syntax highlighting and auto-completion
  - Breaking change detection
  - Performance optimization suggestions

Workflow Engine (Java/Kotlin)

Responsibilities:
  - Task orchestration and scheduling
  - Workflow state management and persistence
  - Error handling and retries
  - Resource allocation and limits
  - Parallel execution coordination

Data Ownership:
  - Dedicated PostgreSQL for workflow definitions and execution state
  - Private Redis cache for active workflow state
  - Local file storage for workflow artifacts, logs, and temporary files
  - TimeSeries DB for workflow metrics and performance data

Patterns:
  - Event sourcing for audit trail
  - SAGA pattern for distributed transactions
  - Circuit breakers for external calls
  - Bulkhead isolation per tenant
  - Compensating actions for rollbacks
  
Scaling:
  - Independent scaling based on workflow load
  - Database partitioning by workflow execution patterns
  - Cache distribution for active workflows
  - Metrics aggregation and rollup strategies

Workflow Scheduler (Go)

Responsibilities:
  - Cron-based workflow scheduling
  - Event-triggered workflow execution
  - Resource-aware scheduling
  - Priority queue management

Features:
  - Distributed scheduling with leader election
  - Timezone-aware cron expressions
  - Workflow dependency resolution
  - Backpressure handling

Data Services

Object Storage

Purpose:
  - Durable storage for large artifacts: conversation attachments, workflow logs, model assets
Capabilities:
  - Versioned buckets/namespaces per service
  - Server-side encryption, lifecycle, and retention policies
  - Pre-signed URL support for controlled client access
Operational Model:
  - Managed via Storage Controller CRDs with per-service PVC/PV or S3-compatible APIs

Search / Indexing

Purpose:
  - Full-text search and relevance over conversations, documents, and DSLs
Capabilities:
  - Ingest pipelines, analyzers per language, synonyms
  - Per-tenant indexes or index partitioning by tenant_id
  - Near-real-time indexing with backpressure controls
Operational Model:
  - Elasticsearch/OpenSearch clusters provisioned via controller, access per service

Vector DB

Purpose:
  - Embedding storage for semantic retrieval and agent memory
Capabilities:
  - HNSW/IVF indexes, metadata filters with tenant_id scoping
  - Batch upserts and range deletes per tenant
  - TTL for ephemeral vectors (session-scoped)
Operational Model:
  - Managed as a dedicated service (e.g., PgVector, Qdrant, Milvus) with per-service logical separation

Infrastructure Controllers

Event Bus Controller

Manages:
  - Pulsar cluster lifecycle (create, update, scale, backup)
  - Multi-tenant topic provisioning per service
  - Auto-scaling partitions based on load
  - Dead letter queue configuration

Provides APIs:
  - Topic creation/deletion per service
  - Message publishing/subscription
  - Schema registry integration
  - Metrics and monitoring endpoints

Database Controller

Manages:
  - PostgreSQL instance lifecycle per service
  - Automated backups and point-in-time recovery
  - Connection pooling and read replicas
  - Security and access control

Provides APIs:
  - Database provisioning per service
  - Connection string management
  - Schema migration support
  - Performance metrics collection

LLM Controller

Manages:
  - GPU resource allocation and scheduling
  - Model lifecycle (download, load, unload, scale)
  - Multiple serving engines (Ollama, vLLM, TGI)
  - Model version management and rollback

Provides APIs:
  - Inference endpoints per model
  - Model selection and routing
  - Usage tracking and cost optimization
  - Health checking and failover

// Local LLM deployment configuration
export interface LocalLLMCluster {
  ollama: {
    replicas: number;
    models: string[]; // ["llama2:7b", "mistral:7b", "codellama:13b"]
    resources: ResourceRequirements;
  };
  vllm: {
    replicas: number;
    tensorParallelism: number;
    gpuMemoryUtilization: number;
  };
  textGenerationInference: {
    replicas: number;
    quantization: "4bit" | "8bit" | "none";
    maxBatchSize: number;
  };
}

// Auto-scaling configuration
export interface ScalingConfig {
  minReplicas: number;
  maxReplicas: number;
  targetCPU: number;
  targetMemory: number;
  accountBasedScaling: boolean;
}

Local LLM Deployment Strategy

Model Management & Deployment

Local LLM Architecture:
  Model Registry:
    - Centralized model storage within customer infrastructure
    - Version management and rollback capabilities
    - Support for custom fine-tuned models
    - Automated model downloading and caching
    
  Serving Infrastructure:
    - Multiple serving engines for different use cases
    - GPU resource pooling and scheduling
    - Auto-scaling based on inference demand
    - Load balancing across model replicas
    
  Security & Isolation:
    - All model inference within customer boundary
    - Tenant-level model access control
    - Encrypted model storage and memory
    - No external data transmission

Deployment Options:
  Small Scale (1-10 users):
    - Single Ollama instance
    - CPU-only inference for basic models
    - Shared GPU resources
    
  Medium Scale (10-100 users):
    - Multiple serving engines (Ollama + vLLM)
    - Dedicated GPU nodes
    - Model-specific auto-scaling
    
  Enterprise Scale (100+ users):
    - Multi-engine deployment with load balancing
    - GPU cluster with tensor parallelism
    - Multiple model variants (7B, 13B, 70B)
    - Advanced caching and optimization

GPU Resource Management

GPU Scheduling:
  Node Pools:
    - Dedicated GPU node pools for LLM workloads
    - Mixed CPU/GPU nodes for general workloads
    - Spot instances for cost optimization (non-critical)
    
  Resource Allocation:
    - GPU sharing for smaller models (MIG)
    - Full GPU allocation for larger models
    - Dynamic resource adjustment based on load
    
  Optimization:
    - Model quantization (4-bit, 8-bit)
    - Batching and continuous batching
    - KV-cache optimization
    - Model parallelism for large models

Service Communication Strategy

Why No Service Mesh

The platform deliberately avoids service mesh (Istio, Linkerd) to preserve Domain-Driven Design principles and prevent distributed monolith anti-patterns:

Domain-Driven Design Conflicts

Service mesh fundamentally conflicts with Domain-Driven Design by externalizing critical service communication concerns:

1. Erosion of Bounded-Context Autonomy

DDD requires each bounded context to encapsulate its own model, language, and lifecycle
Service mesh moves interaction semantics (routing, retries, policies) out of domain code into platform control
Domain teams lose ownership and evolution control over their own communication patterns
Creates dependency on platform teams for domain-specific communication needs

2. Loss of Internal Coherence

DDD emphasizes ubiquitous language and consistent models within each context
Mesh routing rules and retry logic live outside codebase, fragmenting the mental model
Developers must check YAML dashboards or CLI commands to understand inter-service contracts
Business logic flow becomes harder to trace and reason about

3. Versioning and Consistency Breakdowns

Domain models evolve through code-repo versioning with coordinated releases
Mesh introduces parallel versioning axis (mesh policies) often unsynchronized with service releases
Creates subtle mismatches where mesh strips fields or routes incorrectly
Undermines consistency guarantees between services

4. Centralized Dependency and Operational Coupling

DDD advocates decentralized governance: teams choose deployment cadence, databases, libraries
Service mesh creates single chokepoint that all domains must traverse
Mesh upgrades, control-plane outages, misconfigurations ripple through all domains
Replaces loose coupling with operational monoculture

Technical Problems with Service Mesh

Hidden Complexity: Network topology becomes opaque and hard to debug
Performance Overhead: Additional network hops and proxy layers
Operational Burden: Complex configuration and troubleshooting
Distributed Monolith: Creates illusion of separation while increasing coupling

Our Domain-Centric Approach Instead

Direct HTTP/gRPC: Simple, traceable service-to-service communication owned by domains
Event Bus Decoupling: Async communication preserving service autonomy
Domain-Owned Contracts: Well-defined APIs maintained within each bounded context
Application-Level Resilience: Circuit breakers and retry logic as domain business rules
Service-Native Observability: Distributed tracing without mesh complexity

This approach maintains domain autonomy while enabling the platform-level benefits (observability, resilience, security) through controller-managed infrastructure rather than communication interception.

CQRS (Command Query Responsibility Segregation)

Each business service implements CQRS internally to optimize for both read and write performance:

API Gateway Role:

Simple Routing: Routes requests to appropriate business service based on path/domain
No CQRS Logic: Does not distinguish between commands and queries
Load Balancing: Distributes load across service instances
Authentication: Validates tokens before forwarding requests

Service-Level CQRS (Within Each Service):

Request Classification: API layer routes based on HTTP method and operation intent
- Queries: GET requests → Query handlers (read-only operations)
- Commands: POST/PUT/DELETE requests → Command handlers (state-changing operations)
Query Handlers: Direct, optimized read paths to data layer with caching
Command Handlers: Business logic processing with validation and event emission
Database Access: Unified access to controller-managed infrastructure

Query Processing (Within Service):

Fast Response: Optimized read paths with caching
Cache-First: Service-level cache before database access
Read Optimization: Dedicated query models and projections

Command Processing (Within Service):

Business Logic: Domain-specific command validation and processing
Event Generation: Commands generate domain events for other services
Data Consistency: Transactional consistency within service boundary
Async Propagation: Events published to inter-service event bus

Benefits:

Service Autonomy: Each service optimizes its own read/write patterns
Clear Boundaries: CQRS boundaries align with business domains
Performance: Service-specific optimizations for queries and commands
Scalability: Services scale command and query sides independently

Kubernetes-Native Service Data Ownership

Each application service logically owns its data but accesses it through infrastructure CRDs managed by Kubernetes controllers:

Service Data Ownership via Infrastructure APIs:

Dedicated Database: Service accesses its own PostgreSQL database via Database Controller CRDs
Private Cache: Service accesses its own Redis instance via Cache Controller CRDs
Event Topics: Service publishes/subscribes to its own topics via Event Bus Controller CRDs
File Storage: Service accesses its own PVC volumes via Storage Controller CRDs
Schema Evolution: Services evolve their data schemas through operator-managed migrations

Infrastructure Controllers:

Database Controller: Manages PostgreSQL instances, backups, scaling, and access CRDs
Cache Controller: Manages Redis instances, clustering, and access CRDs
Event Bus Controller: Manages event bus clusters, topics, and messaging CRDs
Storage Controller: Manages persistent volumes, snapshots, and access CRDs
LLM Controller: Manages model serving, GPU allocation, and inference CRDs

Kubernetes-Native SDLC Benefits:

Operator-Managed Infrastructure: Database/cache scaling and maintenance handled by operators
Service Independence: Services deploy independently while infrastructure evolves separately
API-Driven Access: Clean abstraction between application logic and infrastructure
Resource Isolation: Each service’s resources are isolated but managed centrally
GitOps Integration: Infrastructure and application configs managed through K8s manifests

Security Architecture

Authentication & Authorization Flow

The platform implements a layered security model that separates authentication from authorization:

External Authentication (IDP Integration)

Identity Provider Integration: OIDC/OAuth2 integration with external IDPs (Auth0, Okta, Azure AD, etc.)
JWT Token Validation: API Gateway validates JWT tokens from trusted IDPs
User Context Extraction: Extracts user identity, tenant context, and claims from validated JWTs
No Internal Auth Service: Leverages external IDPs rather than maintaining internal user credentials

API Gateway Security Layer

Token Validation: Validates JWT signatures, expiration, and issuer claims
Request Routing: Routes authenticated requests to appropriate business services
Tenant Context Injection: Adds extracted tenant/user context to service requests
Rate Limiting: Applies per-tenant rate limiting based on authenticated identity

Runtime Authorization Engine (Platform Service)

Tenant Access Control: Fine-grained authorization for tenant-specific resources
Policy Management: Centralized policies for document, workflow, and conversation access
Resource-Level Permissions: Controls access to specific conversations, workflows, agents
Dynamic Policy Evaluation: Real-time authorization decisions based on tenant context

Service-Level Authorization

Tenant Authorization Checks: Each service validates tenant access via Platform Service authorization engine
Resource Ownership: Services verify that tenants can only access their own resources
Cross-Service Validation: Authorization context propagated across service boundaries
Audit Trail: All authorization decisions logged for compliance and debugging

Zero-Trust Security Model

Identity Verification: OIDC integration with major IDPs
Secret Management: K8s secrets + cloud KMS integration
Network Security: Direct service-to-service authentication with mTLS
Data Encryption: At-rest and in-transit encryption
Audit Logging: Comprehensive audit trail
Local LLM Security: All inference within customer infrastructure boundary

Multi-Tenant Isolation

Network Isolation: Kubernetes namespaces per tenant tier
Data Isolation: Hash-based partitioning
Resource Isolation: ResourceQuotas and LimitRanges
API Isolation: Request routing based on account context

Scalability Considerations

Auto-Scaling Triggers

HorizontalPodAutoscaler:
  - CPU utilization > 70%
  - Memory utilization > 80% 
  - Custom metrics: conversations/sec
  - Event queue depth per tenant

VerticalPodAutoscaler:
  - Memory-intensive AI operations
  - Variable LLM response sizes
  - Dynamic resource requirements

ClusterAutoscaler:
  - Node capacity based on tenant growth
  - Multi-zone availability requirements

Performance Targets

Conversation Response: < 2s for simple queries
DSL Generation: < 30s for complex workflows
Workflow Execution: Depends on workflow complexity
System Throughput: 10,000+ concurrent conversations

Technology Stack Summary

Layer	Technology Choices	Rationale
Infrastructure Controllers
Database Controller	PostgreSQL Controller	Automated database provisioning, scaling, backups
Cache Controller	Redis Controller	Automated cache clustering, failover, scaling
Event Bus Controller	Event Bus Controller	Multi-tenant topics, auto-scaling, geo-replication
Storage Controller	K8s CSI + Storage Controller	Automated PVC provisioning, snapshots, lifecycle
LLM Controller	Custom LLM Controller	GPU scheduling, model lifecycle, inference CRDs
Application Services
Service Runtime	Container workloads (Pods)	Stateless, scalable, controller-managed infrastructure access
Service Data Access	Controller-provided CRDs	Clean abstraction, automated scaling, isolation
Service Communication	Direct HTTP/gRPC + Event CRDs	Simple, traceable, avoiding distributed monolith
Platform Infrastructure
Container Orchestration	Kubernetes + Custom Controllers	Cloud-agnostic, declarative, automated operations
Cloud LLM Integration	OpenAI, Anthropic, + Others	Vendor flexibility, cost optimization
GPU Management	NVIDIA GPU Operator + Custom Scheduling	Resource pooling, multi-tenancy, automatic allocation
Infrastructure as Code	CDK8s + Controller Manifests	Type-safe, GitOps-ready, controller-driven deployments

Phased Implementation Strategy

Architectural Philosophy: Cloud Reliability + Bare Metal Security

The platform follows a technology-agnostic design that enables customers to choose their desired balance between cloud convenience and infrastructure control:

Core Design Principles

Pattern-Based Architecture: Focus on architectural patterns that can be implemented across different technology stacks
Infrastructure Abstraction: Business services use interfaces, not concrete implementations
Progressive Infrastructure Control: Start with managed services, evolve to full control as needed
Security-First Flexibility: Enable bare metal-level security while leveraging cloud reliability

Strategic Benefits

Cloud Managed Services Advantages:

Reliability: Battle-tested, enterprise-grade infrastructure with SLAs
Development Velocity: Teams focus on business logic, not infrastructure management
Cost Efficiency: Pay-per-use scaling without upfront hardware investments
Security Updates: Automatic patching and security maintenance

Bare Metal Control Advantages:

Data Sovereignty: Complete control over data location and access patterns
Custom Security: Implement specialized compliance and security requirements
Performance Optimization: Direct hardware access for AI/ML workloads
Cost Control: Eliminate cloud markup for predictable, large-scale workloads

Our Approach: Customers start with cloud managed services for rapid deployment and feature development, then gradually migrate to self-managed infrastructure as security, compliance, or cost requirements demand greater control.

Phase 1: ECS + AWS Managed Services (POC & Business Logic Focus)

Goal: Rapid POC development with maximum business logic focus and minimal infrastructure complexity

Why ECS First:

Simplest Container Orchestration: No Kubernetes complexity, just containers and services
CDK Consistency: Same CDK patterns as later EKS phases enable smooth iteration
AWS Integration: Native integration with RDS, ElastiCache, EventBridge
Fast Development Cycles: Deploy and test business logic without platform overhead
Resource Efficiency: Lower resource requirements for initial development
Proven Reliability: Battle-tested AWS container orchestration

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Phase 1 Benefits:

Maximum Development Velocity: Teams focus 100% on business features, not infrastructure
CDK Infrastructure as Code: Consistent patterns that carry forward to later phases
Service Isolation: Each microservice gets dedicated AWS resources (RDS, Cache, etc.)
Parallel Development: Business logic and platform architecture evolve independently
Cost Effective: No K8s overhead or complex operator development needed

Phase 2: EKS + AWS Managed Services (K8s Migration)

Goal: Migrate to Kubernetes while maintaining AWS managed service simplicity

Why EKS Next:

K8s Learning Curve: Teams learn K8s gradually without infrastructure management burden
CDK Consistency: Same CDK patterns, just targeting EKS instead of ECS
Maintained Service Isolation: Each service still uses dedicated AWS resources
Advanced Orchestration: Better scaling, networking, and service management
Ecosystem Access: Access to K8s ecosystem tools and operators

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Phase 3: XKS + Custom Operators (Multi-Cloud Kubernetes)

Goal: Achieve cloud portability while building custom infrastructure automation

Why XKS (Extended Kubernetes):

Cloud Agnostic: Support EKS, AKS, GKE with unified deployment patterns
Custom Operators: Begin developing platform-specific operators for specialized needs
Gradual Infrastructure Ownership: Migrate from managed services to custom operators service-by-service
Multi-Cloud Strategy: Enable customer choice of cloud provider
Advanced Features: Leverage cloud-specific K8s extensions (EKS Fargate, AKS Virtual Nodes, GKE Autopilot)

Phase 4: Bare Metal + Full Custom Operators (Maximum Control)

Goal: Complete infrastructure ownership and maximum customer deployment flexibility

Why Bare Metal Last:

Maximum Performance: Direct hardware access for AI/ML workloads
Cost Optimization: Eliminate cloud markup for large-scale deployments
Data Sovereignty: Complete control over data location and access
Custom Hardware: Support for specialized AI chips, custom networking, storage
Enterprise Requirements: Meet strict compliance and security requirements

Infrastructure Abstraction Pattern

// Business services use interfaces, not concrete implementations
export interface DatabaseService {
  query(sql: string, params: any[]): Promise<any[]>;
  transaction(callback: (tx: Transaction) => Promise<void>): Promise<void>;
  // Service doesn't care if it's RDS or PostgreSQL Operator
}

export interface CacheService {
  get(key: string): Promise<string | null>;
  set(key: string, value: string, ttl?: number): Promise<void>;
  // Service doesn't care if it's ElastiCache or Redis Operator
}

export interface EventService {
  publish(topic: string, event: any): Promise<void>;
  subscribe(topic: string, handler: (event: any) => void): void;
  // Service doesn't care if it's EventBridge or Pulsar
}

// Phase 1: AWS implementations
export class RDSService implements DatabaseService { /* AWS RDS */ }
export class ElastiCacheService implements CacheService { /* AWS ElastiCache */ }
export class EventBridgeService implements EventService { /* AWS EventBridge */ }

// Phase 2: K8s operator implementations  
export class PostgreSQLOperatorService implements DatabaseService { /* K8s PostgreSQL */ }
export class RedisOperatorService implements CacheService { /* K8s Redis */ }
export class PulsarOperatorService implements EventService { /* K8s Pulsar */ }

Technology-Agnostic Migration Strategy

Implementation Flexibility: The platform’s interface-based design enables customers to choose their infrastructure stack based on their specific requirements:

Development Approach:

Interface-First Design: All infrastructure dependencies defined as abstract interfaces
Multi-Implementation Support: Same business logic works across AWS, Azure, GCP, or bare metal
Customer Choice: Deploy on preferred infrastructure without code changes
Seamless Evolution: Migrate between implementations as requirements change

Deployment Options:

Cloud-First: Start with managed services (RDS, ElastiCache, EventBridge)
Hybrid: Mix managed services with self-hosted components as needed
Self-Hosted: Full Kubernetes operators for complete infrastructure control
Bare Metal: Direct hardware deployment for maximum performance and security

Risk Mitigation:

No Vendor Lock-in: Interface abstraction prevents coupling to specific providers
Business Logic Stability: Core application code remains unchanged across deployments
Proven Patterns: Start with battle-tested services, evolve to custom solutions
Incremental Migration: Change infrastructure components independently

This phased approach enables rapid business value delivery while maintaining the end-goal of a fully self-contained, customer-deployed platform. Teams can focus on domain logic while infrastructure matures at its own pace.