RAG Architecture for the Enterprise AI Factory: Designing Secure, Governed and Scalable Retrieval-Augmented Systems with GPT-5.5, Opus 4.8 Agentic Workflows

Frontier models have become a commodity. Governed access to your enterprise's own knowledge has not - and that is where durable advantage now lives. RAG Architecture for the Enterprise AI Factory is a playbook for turning Retrieval-Augmented Generation from a scatter of impressive demos into a secure, governed, reusable capability that many products can draw on. Written for CIOs, CTOs, chief and solution architects, and LLMOps teams, it operates at the altitude of the people who must fund, secure, and live with AI in production. Across ten chapters it delivers a complete reference architecture: governed knowledge ingestion and data governance; hybrid retrieval, reranking and context engineering; security, trust and compliance by design (IAM, RBAC/ABAC, GDPR, prompt-injection defense, immutable audit); a multi-model strategy across GPT-5.5, Opus 4.8 and beyond; continuous evaluation and observability; agentic RAG and workflow automation; multimodal RAG and GraphRAG; and the operating model - teams, governance, FinOps and a 30/60/90-day roadmap - that makes it scale. Every chapter follows the same structure - the bottom line, the corporate problem, the key decisions, recommended patterns and anti-patterns, a Solution Architect checklist, and expected deliverables - and is illustrated with executive-grade exhibits. The book grew out of a real enterprise project - retrieval-augmented automation of a Cegid Retail accounting flow - and sets down, as architecture rather than anecdote, what it takes to make a loop trustworthy enough to own a business outcome. Stop building one more chatbot. Start building the trust layer of your AI Factory.