AI Leadership for the Agentic Era
I build engineering organizations where AI is a core operating layer, not a tool bolted on top of the SDLC.
Most Companies Have AI Pilots. Few Have Production.
AI Experiments
Your team has tried Copilot. Some engineers love it. Most workflows have not changed. Productivity gains are anecdotal. There is no governance. The CFO has questions.
The Gap
No strategy. No governance framework. No measurement. No clear path from "we use AI tools" to "AI is embedded in how we deliver software." This gap widens every quarter.
Production AI
Agentic systems handling PR reviews. CI/CD pipelines with intelligent quality gates. AI agents onboarding new engineers in days. Governance dashboards the CFO can trust. This is where you need to be.
The Context Revolution
AI does not operate on abstraction. It operates on context. The organizations winning with AI in 2026 are not the ones with the most tools - they are the ones that have built the richest, most structured context for their agents to operate within.
AGENTS.md. CLAUDE.md. Markdown-native documentation. Structured memory systems. When context is infrastructure, AI agents stop being assistants and start being contributors - reviewing code with full understanding of your architecture, onboarding engineers with institutional memory intact, and operating within boundaries your team controls.
Context as Infrastructure
AGENTS.md and CLAUDE.md files versioned alongside code. AI agents operate within documented constraints your team defines, reviews, and controls - not black-box prompts.
Markdown as Substrate
Documentation structured for both humans and agents. Written so AI can reference it, reason over it, and act on it reliably - not just surface keywords.
Versioned Agent Memory
Agent instructions that evolve with your codebase. Not static prompts - living operational context checked into source control alongside the code it governs.
What I Build and Deploy
Agentic AI Orchestration
I build production-grade multi-agent systems using MCP (Model Context Protocol) and A2A protocols. These are not demos. They are operational systems embedded throughout the engineering lifecycle with SLOs, audit trails, and rollback plans.
- MCP server implementation connecting agents to codebases, CI/CD, observability tools, and internal APIs
- Multi-agent orchestration for code review, testing, documentation, and deployment workflows
- 70% of routine PR reviews AI-assisted in production deployments
- 55% reduction in developer toil measured across teams I have led
AI Governance and FinOps for AI
Scaling AI without governance creates uncontrolled costs, compliance exposure, and board-level risk. I build the accountability framework that makes AI scaling safe, auditable, and CFO-ready.
- Agent SLOs and reliability reporting for board-level visibility
- FinOps dashboards tracking AI costs per team and per workflow
- Human-in-the-loop policies defining what agents act on autonomously
- Audit trails satisfying compliance and regulatory requirements
Internal Developer Platforms (IDPs)
An IDP is the foundation that makes AI-native engineering sustainable. I build self-service platforms giving engineers governed access to CI/CD, observability, secrets management, and AI agent tooling without filing tickets or waiting on infrastructure teams.
- 55% developer toil reduction through self-service automation
- 4x faster new engineer onboarding with documented, reliable tooling
- AI agent integration as a first-class IDP capability
- DORA metrics instrumented across the platform from day one
AI-Native Software Delivery
AI-native does not mean adding a Copilot subscription. It means redesigning workflows so AI is embedded at every phase: architectural exploration, pair programming, intelligent testing, automated documentation, and deployment pipelines.
- Claude for architectural brainstorming and design exploration
- Cursor and GitHub Copilot for AI-assisted pair programming
- Intelligent testing and automated QA validation workflows
- MLOps pipelines that treat model deployment with production rigor
Cross-Functional AI Transformation
AI transformation that stops at Engineering misses most of the value. The highest-ROI AI deployments cross organizational lines - connecting Engineering, Product, Operations, Finance, and HR into a unified AI-native operating model. I design and lead transformation programs that extend AI capability across the full organization, not just the SDLC.
- Operations: AI-assisted process automation and workflow optimization
- Finance: FinOps dashboards and AI cost attribution across teams
- Product: AI-accelerated discovery, prioritization, and roadmap analysis
- HR: AI-supported hiring, onboarding, and talent development programs
- Engineering: Agentic workflows across the full SDLC
- Leadership: Executive AI literacy and organizational change management
Outcomes From Production AI Deployments
"Strategy without execution is hallucination."
AI strategy that does not ship to production is just another slide deck. I build production systems, governance frameworks, and the engineering culture that makes AI transformation real and measurable.
Why Engineering Leaders Are Making This Move Now
Attract and Retain Top Engineers
Engineers want to work with modern AI tooling. Teams with AI-native workflows have a recruiting advantage that compounds over time. Falling behind on tooling means falling behind on talent.
Competitive Velocity
Teams deploying AI-assisted development, testing, and review cycles ship faster and with fewer defects. The velocity gap between AI-native and traditional engineering widens every quarter.
Board and CFO Confidence
Without governance, AI investment is a cost center with no visibility. With the right framework, AI becomes a measurable operational asset. I build both the capability and the accountability layer.
Tools and Technologies
Ready to Move From AI Experiments to Production AI?
Whether you are starting your AI strategy or scaling what you have already built, I bring the architecture, governance, and engineering leadership to make it real and measurable.