I design agentic AI systems that survive production.

Architecture, data foundations, and operating practices for enterprises moving past the demo stage. This site is part portfolio, part digital garden — the thinking is public as it grows.

Work with me See the work Explore the garden

AI systems — the focus

Most enterprise AI stalls between demo and production — because production AI is an architecture, data, and governance problem, not a model problem. That seam is where I work: agent architectures with properly designed memory, the data platforms that give agents something real to act on, and the evaluation and operating practices that keep them trustworthy.

• Agentic AI

  Agent architectures, memory design as a data modeling problem, evaluation harnesses, and failure-mode analysis for systems that act on our behalf.

• Data platforms for AI

  Lakehouse patterns, data modeling, and governance — the foundations that decide whether an agent system has anything true to work with.

Grounded in

The AI work stands on two decades across the full stack of practice: architecture & systems · product & strategy · UI/UX & design · platform engineering · teaching & training. The breadth is why the AI systems hold together; the garden holds the patterns from all of it.

Freshly tended

• Agent frameworks are the new ORMs — the abstraction debt comes due at production Agentic AI
• The LLM gateway is a control point, not plumbing Agentic AI
• Code-as-action beats JSON tool calls once tasks compose Agentic AI
• Drift detection is the production eval — offline suites only catch what you predicted Agentic AI
• Agent memory needs governance before it needs embeddings Agentic AI

Now: building production-grade agentic AI reference systems and writing up the patterns as they prove out. What I'm focused on