15+ years in enterprise data, now focused on what's above the dashboard: multi-agent systems built on Claude, autonomous DataOps that self-heals, and AI decision layers grounded in governed data. I build on the Claude API in production — not as a demo, but as the reasoning engine for systems that run without human intervention.
I'm a practitioner, not a prompt engineer. I use the Claude API as the reasoning backbone for multi-agent systems that coordinate through shared state, respect confidence thresholds, and escalate gracefully when they're uncertain. I write about what it actually takes to build safely and reliably on top of LLMs.
At Workday I lead BI Analytics and shipped an AI Companion — natural-language queries over marketing performance data, grounded in a governed Snowflake semantic layer. The same architecture principles apply to any system where you need LLM reasoning to respect data contracts and organizational governance.
My three Claude-native projects — Pipeline Sentinel (autonomous DataOps), Growth Intelligence Agent (agentic analytics), and LoanLens (AI-narrated investor memos) — are production-grade systems with real confidence gates, audit trails, and fallback logic. That's the kind of engineering judgment I bring to agentic systems work.
I'm actively talking to teams building the next generation of agentic AI infrastructure — systems that coordinate multiple models, ground reasoning in governed data, and escalate gracefully when uncertain. I build on Claude in production and I know what it takes to make these systems reliable.