AI Architect 101
A practical guide to enterprise AI architecture
This series explains how organizations move beyond AI demos and design systems that are reliable, governed, secure, observable, and connected to real business operations.
Series Roadmap
From experiments to AI operations
Enterprise AI architecture is not one decision. It is a stack of decisions across knowledge, models, tools, governance, security, evaluation, and organizational ownership.
The roadmap starts with the full system view, then goes deeper into the individual layers required to make AI useful in production.
AI Architect 101: Building Enterprise AI Systems That Actually Work
PublishedThe foundation: why enterprise AI needs architecture, governance, security, and operations.
RAG, GraphRAG, and Knowledge Systems
PublishedHow AI systems retrieve, structure, and reason over enterprise knowledge.
Agentic AI and Multi-Agent Systems
PublishedHow agentic systems plan, use tools, coordinate workflows, and create new governance needs.
AI Governance and Risk Management
PublishedThe ownership, policy, audit, and approval layers required for trustworthy AI.
Enterprise AI Security
PublishedIdentity, authorization, data protection, prompt injection, tool abuse, and secure architecture.
AI Observability, Evaluation, and Guardrails
PublishedTracing, feedback, quality measurement, hallucination detection, guardrails, and system evaluation.
MCP, APIs, and Enterprise Integrations
PublishedHow AI systems connect to business tools, APIs, workflows, and controlled enterprise actions.
Building an AI Operating Model
PlannedThe teams, processes, governance, and measurement needed to operate AI at scale.
Architecture Pillars
The layers every enterprise AI system needs
These pillars are the recurring decisions behind dependable AI systems: what the system is for, what it knows, what it can do, how it is controlled, and how it improves.
Business Alignment
Tie AI systems to measurable outcomes, workflows, owners, and decision points.
Knowledge Systems
Design retrieval, metadata, search, and knowledge structures that make AI useful.
Agentic Workflows
Control how agents plan, use tools, coordinate tasks, and escalate decisions.
Governance
Define policy, traceability, approvals, risk controls, and model lifecycle practices.
Security
Make AI inherit enterprise identity, authorization, and data protection controls.
Observability
Track prompts, retrieval, tools, cost, latency, feedback, and quality signals.