AI delivery governance & agentic operating models
Vitalii Oborskyi · Delivery leader, researcher & author.
I help software organizations investigate why AI adoption is not improving delivery, rebalance review and ownership, and define controls for agentic systems in production.
For enterprise software teams and IT services companies.
AI is a fundamental shift: an attempt to delegate parts of thinking to machines.
More than 20 years in software engineering and technology delivery, including over a decade in leadership. Delivery and PMO responsibility across a portfolio of around 25 projects and more than 120 engineers. That experience shapes my research into software delivery and systems that depend on model judgment.
01 / Where I can help
Delivery / Diagnosis
Assess the delivery flow, review and verification capacity, rework, knowledge dependencies and costs. Investigate the actual constraint.
Possible output: A prioritized diagnosis of delivery constraints and a plan for testing the next changes.
Architecture / Runtime governance
Review delegated judgment, permissions, limits, evaluation evidence, cost ownership, escalation and corrective action.
Possible output: A review of decision authority, operating limits and evidence gaps, with priorities for addressing them.
SDLC / QA / Operating model
Work through requirements, evaluation, release decisions and human capacity in a focused workshop.
Possible output: draft role and decision boundaries, learning routines and an improvement backlog.
Potential outputs, depending on the agreed scope. The first conversation determines fit and scope. Discuss your AI challenge →
02 / Research
What happens when machines generate faster than people can understand and verify? And what changes when the software itself relies on model judgment?
01 / How we build
I see the AI-assisted SDLC as structurally unbalanced where generation accelerates faster than human understanding, verification and ownership. More output can coexist with review queues, rework and fragile maintenance.
The Subprime Code Crisis investigates this gap and how teams can rebalance generation, verification and ownership. It treats proposed responses as hypotheses to test in context.
Start with Externalization & the verification crisis.
02 / What we build and operate
Uncertainty Architecture studies what I call Thinking Systems: software in which consequential runtime responsibilities depend partly on probabilistic model judgment. This is where I see the fullest expression of today's AI potential — and some of its hardest engineering problems.
The controlled object is the software system. Its control perimeter also includes human roles, evidence, decision authority and corrective action. It needs development lifecycles and operating models suited to uncertainty. Much remains to develop and test.
Start with Thinking Systems.
03 / Selected writing
Thinking Systems for architecture; Externalization for generation and verification; Moat for strategic differentiation. All five selected editions are English.
LinkedIn edition · 27 Aug 2026
What changes when consequential runtime responsibility depends on model judgment.
Why faster generation raises questions of human understanding and verification.
A control-theory perspective on agentic loops and engineering control.
A bounded technical example of semantic verification within a wider control architecture.
Where durable differentiation can sit in AI-native workflows.
04 / Selected public responses
A selection of public responses to Uncertainty Architecture.
EPAM · Principal Founder & Executive Chairman
Recommended reading Thinking Systems and extended its argument toward bounded architecture and runtime control.
Co-author of Team Topologies
Reshared Michael Risch’s discussion of my AI governance article and highlighted governance as bringing business intent back into the system.
CPMAI Lead Coach · PMI AI Standards Core Team
Connected UA’s control-theory framing with CPMAI practices, roles and governance gates.
UA advisors: Markus Kopko — governance and alignment; Otman Basir — academic.
Full discussion & source context ↗05 / About
My work has included building delivery functions, resource planning, risk governance and organizational design. It shapes the questions I bring to AI: what teams need to understand, how decisions are made, and who owns the outcome.
I write for architects, engineering and delivery leaders, product owners and PMO practitioners. This site connects the publications and living research behind that work.
Invited speaker at Corning’s internal technical AI workshop. Explore the talk ↗
This site is also a practical example of human-directed, AI-assisted delivery. About the build ↗
06 / Contact
Bring a system, a delivery bottleneck, or a question you cannot yet frame. We can discuss where the uncertainty sits and what a useful next step could look like.
Research collaboration, speaking invitations and professional opportunities are welcome too.
Choose a time for a conversation, or send me an email.
Book a conversationPrefer email? oborskyivitalii@gmail.com