Enterprise Architecture
Body of Knowledge
Reference architectures, frameworks, patterns, and taxonomies spanning distributed systems, cloud-native platforms, modernization, and enterprise AI.
Enterprise Architecture
How enterprise systems evolve—from monoliths to distributed systems to intelligent platforms.
Reference Architectures
Comprehensive blueprints for large-scale enterprise systems
Event-Driven Enterprise Architecture
Designing loosely coupled systems around domain events, asynchronous messaging, and state propagation at scale
Coming SoonCloud-Native Platform Reference Architecture
Blueprint for resilient, horizontally scalable platforms built on containers, managed services, and automation
Coming SoonModernization Reference Architecture
Structured path from monolith to modular services to cloud-native platform—drawn from the Springer book
Coming SoonArchitecture Frameworks
Reusable reasoning tools for making durable architectural decisions
Operational Constraints Framework
Designing systems around latency, cost, compliance, throughput, and reliability before technology selection
Coming SoonArchitecture Decision Framework
Business outcome → operational constraints → architecture pattern → technology choice. A repeatable decision chain.
Coming SoonDistributed Systems Reliability Model
Active-active resilience, graceful degradation, and observability as first-class reliability concerns
Coming SoonArchitecture Patterns
Reusable solutions to recurring distributed-systems problems
Strangler Modernization Pattern
Incrementally replacing a monolith by routing capabilities to new services over time
Coming SoonActive-Active Resilience Pattern
Multi-region, multi-instance operation that survives partial failure without downtime
Coming SoonEvent-Carried State Transfer
Propagating state through events to reduce synchronous coupling between services
Coming SoonDomain Event Integration Pattern
Integrating bounded contexts through well-defined domain events rather than shared databases
Coming SoonEnterprise AI
Designing governable, observable AI systems that combine deterministic workflows with probabilistic reasoning.
AI Reference Architectures
End-to-end blueprints for production AI systems
AI Frameworks
Structured approaches for understanding and designing AI systems
Context Engineering Framework
How to design context strategies that combine prompt engineering, RAG, tool-calling, and memory management
Coming SoonAI Maturity Model
Progression model for enterprise AI capability development
Coming SoonEnterprise Agent Governance Model
Framework for governing autonomous AI agents in enterprise environments
Coming SoonAI Patterns
Reusable solutions to common AI architecture problems
Production Control Loop
Validate inputs, measure context quality, evaluate outputs, monitor trends, escalate when risk patterns change
PublishedDeterministic vs Probabilistic Systems
When to use workflows vs reasoning in enterprise systems—the 2x2 decision matrix
PublishedAI for Legacy Systems
Using AI as an observability, explanation, and support layer for legacy enterprise systems without risky rewrites
PublishedAI Taxonomies
Classification systems for understanding AI components
About This Body of Knowledge
This body of knowledge reflects 20+ years building large-scale enterprise platforms—spanning distributed systems, cloud-native architecture, modernization, and enterprise AI. It is designed to be referenced, cited, and applied.
Suggested Citation
Rajasekharaiah, Chandra. Enterprise Architecture Body of Knowledge. rcmohan.com/frameworks