AI delivery governance & agentic operating models
Vitalii Oborskyi · Enterprise delivery leader & independent research author.
My work combines enterprise software delivery leadership with independent research into how AI changes engineering, operating models and the systems we build.
Research, publications, talks and practical engineering experiments.
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 / Professional contribution
Delivery / Transformation and verification
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.
Governance / Agentic operating models
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.
Leadership / Capability and research
Work through requirements, evaluation, release decisions and human capacity through organizational work, applied research or focused workshops.
Possible output: draft role and decision boundaries, learning routines and an improvement backlog.
Examples of work, depending on the role, organization and agreed scope. Start a conversation →
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.
These entries summarize public discussions of specific publications. They do not imply endorsement of this website, the research programme as a whole, or the author’s services. Organizational affiliations are provided for identification only.
Arkadiy Dobkin · EPAM founder
In his public response to Thinking Systems, Arkadiy recommended reading the article and added two propositions: a broader market for model-mediated software and differentiation through domain-specific runtime control architectures.
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 brings together my publications, talks and independent research.
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
I’m open to conversations about enterprise leadership opportunities, research collaboration and selected advisory work where my experience and research may be relevant.
Speaking invitations and practical questions about AI delivery, engineering and governance are welcome too.
Choose a time for a conversation, or send me an email.
Book a conversationPrefer email? oborskyivitalii@gmail.com