Enterprise Architect — Systems, Governance, and Engineering Organizations
Architecture is a practice, not a diagram.
Most architectural failure isn’t dramatic. It accumulates quietly — in deferred decisions, implicit assumptions, and systems that work until they have to explain themselves.
I focus on the discipline of building systems that remain knowable and changeable as they grow. The writing here examines where that discipline holds, where it breaks, and what it costs when it doesn’t.
Applications — practitioner diagnostics. Theory — foundational work, longer horizon.
I take a small number of fractional advisory engagements. Advisory →
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August 24, 2026 — ApplicationsGovernance Under Scale — Part IV: Model Selection, NAIC, and the Crosswalk
Every framework that now governs enterprise AI asks the same question, and none of them ask it of the model. NAIC, NIST, ISO, the EU, and the bank regulators want to know whether …
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April 24, 2026
Governance Under Scale — Part III: Revocation and the Reachable Decision Surface
Part II separated visibility from control. A system can observe its own drift, make its behavior legible, and surface deviation across time and risk class, and still not be …
March 24, 2026When AI Systems “Dream”: A Failure of Architecture, Not Models
A surprising number of AI failures in production are not hallucinations.
They are systems behaving correctly inside the wrong structure.
March 19, 2026Governance Under Scale — Part II: Monitoring Is Not Control
Part I established that human override is not an external safeguard. It operates inside the system, as a delegation surface through which authority expands when it is not …
March 17, 2026Agents Are Actors With Intent, Not Guarantees
“Agent” has become the default term for a new class of AI systems.
Agentic workflows. Autonomous tools. Systems that can “think” and “act.”
The language suggests something …
March 10, 2026Ghost in the Machine: Adversarial Priors in AI Systems
Large language models are usually described as neutral systems that require alignment. The premise is simple: train a model on a large corpus of human text, then apply guardrails, …
March 2, 2026Governance Under Scale — Part I: Human Override Is Not Governance
In most enterprise AI deployments, “human in the loop” is treated as a safety guarantee. Put a reviewer in front of a probabilistic system and its output is assumed to become …
February 24, 2026AI in Regulated Systems: Where Architecture Becomes Governance
AI is already operating inside regulated control environments.
The risk surface is larger than most teams realize.
In financial services, payments, lending, and compliance-heavy …
February 17, 2026AI Is Increasing Your Delivery Velocity — and Moving Your Problems Downstream
AI didn’t remove your architecture problems.
It moved them.
AI has increased how quickly most teams can produce working software.
Features move from idea to demo in days instead of …
February 1, 2026Dreaming Systems and the Misdiagnosis of AI Failure
Section 1: Hallucination vs. Dreaming
When an AI system produces an incorrect result, the industry almost universally labels the behavior a hallucination. The term has become a …
January 24, 2026When Inference Becomes Control
1. Observation: AI Feeding on AI Across the industry, a familiar pattern is emerging. Systems built with AI components increasingly rely on other AI systems to supervise, evaluate, …
January 6, 2026AI Is a Delivery Tool, Not a Strategy
AI is best understood as a delivery accelerator — not a replacement for architectural thinking, not a substitute for engineering judgment, and not a shortcut around discipline.