A practical framework for building trustworthy, scalable, and governable AI platforms — the field guide I wished existed while doing this work in production.
I’m writing the book on how to design secure, governed, and scalable AI systems for the enterprise: the architecture principles, the runtime, the governance, and the hard-won patterns that separate an AI demo from AI you can put in front of a board, an auditor, and a customer.
Most organizations can stand up an impressive AI prototype. Far fewer can put one into production and defend it — to their board, their auditors, and their customers — when something goes wrong. The gap isn’t the model. It’s the architecture around it: how facts are retrieved, how actions are governed, how identity and data are isolated, and how every decision is made observable and accountable.
This book is a practical, vendor-neutral framework for closing that gap — drawn from building multi-tenant, agentic, retrieval-grounded platforms in a regulated industry. It’s written for the architects, engineering leaders, and executives who have to make AI work, safely and at scale.
At the center sits the AI Platform — secure, governed, observable, and trusted by design — surrounded by the seven domains the book works through in depth:
Principles first · Trust by design · Value at scale.
Want to go deeper? The public Enterprise AI Architecture Framework (EAAF) page gives a free preview of the model, and the Insights develop many of the book’s ideas chapter by chapter.
A work in progress — here’s the shape of it so far.
The case for putting architecture, not the model, at the center.
DraftedFrom prototype to production: what actually breaks, and why.
DraftedHow AI-native systems are put together so they hold up under change.
DraftedThe second layer that keeps systems trustworthy as they act and evolve.
DraftedPolicies, guardrails, human oversight, and risk-tiered automation.
DraftedThe remaining EAAF domains — in active writing.
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