A prototype can work with one model and one API key, but enterprise systems must survive provider failures, data restrictions, security reviews, changing models and shared-platform demand.
FOUNDATIONENTERPRISE AI ARCHITECTURE
What is Enterprise AI Architecture?
Enterprise AI architecture is the set of patterns, boundaries, platforms and operational decisions used to build AI systems that can be trusted, scaled and operated across an organization.
A company standardizes identity, AI gateway, telemetry and approved model access while product teams remain free to build different RAG and agent workflows.
Which architectural decisions should be standardized across every AI product, and which should remain product-specific?
REMEMBEREnterprise architecture creates reusable guardrails without turning every AI product into the same product.
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Enterprise Architecture · Knowledge Map
Explore the complete architecture track.
Jump directly into any architecture domain instead of walking the course one lesson at a time. Every lesson below is a normal HTML link, giving learners — and search crawlers — a direct path into the full Enterprise Architecture curriculum.
107 lesson pages
10 architecture domains
Direct crawl paths from this hub
Foundations & Reference Architecture10 lessons
01The Enterprise AI Stack05Reference Architecture vs Solution Architecture06Enterprise AI Architecture Principles19Define the Platform Boundary28Enterprise RAG Reference Architecture40Enterprise Agent Reference Architecture52Enterprise MCP Reference Architecture61Self-Hosted Inference Reference Architecture103Architecture Decision Records106Architecture Effectiveness Metrics
Model Serving, Gateway & Platform14 lessons
12Architecture from the Latency Budget39RAG Caching43Tool Gateway54MCP Gateway62Inference Pools64GPU Node Architecture65Inference Autoscaling66KV Cache Architecture69Highly Available Inference70Model Serving Rollouts71Inference Observability Architecture79Provider Diversity96Blueprint: Self-Hosted Inference Platform99Blueprint: Batch AI Processing
RAG, Data & Retrieval13 lessons
02Control Plane vs Data Plane29RAG Ingestion Pipeline30Source of Truth31Chunking Architecture32Embedding Service33Vector Store Architecture34Hybrid Retrieval35Metadata Filtering and Authorization36Reranking Layer37RAG Context Assembly38Citation Architecture76Audit Data Architecture90Blueprint: Internal Knowledge Assistant
Agents, State & Workflows14 lessons
41Agent Execution Loop45Agent State Architecture46Agent Memory47Multi-Agent Architecture48Agent Orchestrator49Event-Driven Agents50Compensating Actions51Agent Degraded Modes72Conversation State Architecture73Workflow State74Long-Term Memory Store92Blueprint: Secure Enterprise Agent93Blueprint: Multi-Agent Workflow100Blueprint: Event-Driven Agent Platform
MCP, Tools & Integration9 lessons
Resilience, Availability & Scaling6 lessons
Security, Identity & Trust6 lessons
Observability, Audit & FinOps3 lessons
Governance & Production Operations1 lessons
Other Architecture Concepts31 lessons
03Shared vs Dedicated AI Components04Logical vs Physical Architecture07Build vs Buy08Capture Functional Requirements09AI Non-Functional Requirements10Architecture by Risk Tier11Data Classification Requirements14Workload and Scale Profile15Data Residency and Sovereignty18Enterprise AI Platform20Developer Interface26Shared Evaluation Service44Human Approval Architecture60Managed Model API Architecture63Model Router67Prefix Caching68Model Parallelism75Context and Feature Services77Retention Architecture78Dependency and Failure Map80Regional Failure Architecture87Network Segmentation88AI Egress Control91Blueprint: Customer-Facing AI Assistant95Blueprint: Multi-Model Platform97Blueprint: Hybrid Managed + Self-Hosted AI98Blueprint: Multi-Region AI Platform101Blueprint: Regulated AI Workload104Enterprise AI Pattern Library105AI Technology Radar107Enterprise AI Architecture Capstone