Enterprise AI Engineering
I write and speak about how enterprise AI gets built.
The work starts below the application surface. I use this model to explain the infrastructure, runtimes, and control layers that make AI usable in production.
Ideas
Start here.
Begin with the core framework, then explore recent essays, build notes, and security briefs on the systems that make enterprise AI work in production.
OpenAI's Deployment Company Is a Lock-In Machine, Not a Consulting Firm
DeployCo solves OpenAI's commoditization problem, not your deployment problem, and the lock-in is by design.
Read the pieceRecent writing
Recent essays, frameworks, and operating notes from the archive.
Why Salesforce's Agent Platform Is Actually Genius Lock-In
Headless 360 shifts lock-in from the UI to the infrastructure — enterprises adopting it without abstraction layers trade visible constraints for invisible ones.
The "Queryable Company" Is a Data Architecture Problem, Not a Startup Superpower
The competitive edge in building AI-native isn't being a startup, it's having intentional data architecture from day one, a discipline most founders lack.
Stop Calling Every Workflow an Agent
Autonomy, recovery, and access boundaries matter. Without them, it is automation wearing new language.
Why MCP Is a Trust-Boundary Problem
The problem is not connector count. It is identity, scope, trust boundaries, and what the runtime is allowed to do.
Watch
Video briefings and walkthroughs.
Architecture explainers, stack walkthroughs, and shorter briefings on what changes when AI moves into enterprise operations.
0:00 Featured briefing
OpenAI's Deployment Company Is a Lock-In Machine, Not a Consulting Firm
OpenAI's $4B Deployment Company isn't consulting, it's the most aggressive vertical integration play in enterprise AI history. Here's what architects need to do before it's too late.
Recent briefings
Talks that extend the written work, not duplicate it.
Why Salesforce's Agent Platform Is Actually Genius Lock-In
Salesforce's Headless 360 is architecturally sound and directionally correct. It's also a masterclass in making your platform the one thing agents can never leave. Here's what enterprise architects need to do about it.
The "Queryable Company" Is a Data Architecture Problem, Not a Startup Superpower
Diana Hu's AI-native company vision is the right destination, but the map runs through enterprise data architecture patterns, event sourcing, semantic layers, data contracts, that the startup world chronically underestimates.
About
Built across enterprise systems, data platforms, and applied AI.
Technical enough for architects and engineers. Clear enough for executive teams making operating bets.
I work where implementation detail meets business consequence: the infrastructure, runtimes, and operating patterns that make enterprise AI usable outside the demo.
The throughline is consistent. Identity, governance, integration, data platforms, runtime design, and execution all have to hold before AI becomes operationally real.