AI transformation · Engineering operating model
Agentic SDLC governance
Designing accountable operating models for coding agents, from planning and execution through validation, escalation, and evidence.
My approach
I lead AI adoption and agentic SDLC governance with a delivery-first approach: define the work, constrain the environment, validate the result, escalate judgment, and preserve evidence that leaders and teams can inspect.
Focus
In practice.
Lifecycle governance
Treat planning, execution, validation, and evidence as one operating system rather than a collection of disconnected AI tools.
Controlled execution
Match agent permissions, environments, and boundaries to the risk and intent of the work being performed.
Independent validation
Use tests, policy checks, review gates, and observable outcomes to verify agent output instead of accepting completion claims at face value.
Escalation and evidence
Make exceptions, human decisions, and audit-ready evidence part of the normal delivery path.
Evidence
Relevant experience.
Technology-wide AI adoption
Led AI adoption initiatives across a technology department, including GitHub Copilot usage and coding-agent availability.
Governance grounded in delivery
Leading governance and guardrails within BigHand’s agentic SDLC proof of concept, covering controlled environments, validation, escalation, and evidence.
A practical governance case study
The selected-work story shows how autonomy and accountable control can be designed as one engineering system.
Read the governance case studyWhere it helps
Problems this
experience addresses.
- Engineering organizations moving from AI assistance toward agentic delivery
- Leaders defining guardrails without removing useful autonomy
- Teams that need observable validation, escalation, and audit-ready evidence
- AI adoption programs that must connect tooling with measurable delivery behavior
Related work
Related work.
Related disciplines
- Agentic SDLC
- AI governance
- AI guardrails
- Coding agents
- Controlled validation
- Audit-ready evidence
Contact
Start a conversation.
If my work is relevant to something you are building, I would be glad to hear from you.