The operating model
How the work gets shipped
Every engagement starts by mapping the real workflow: inputs, decisions, exceptions, owners and the places where AI can safely remove manual drag. That map becomes the architecture, not a slide that gets forgotten.
The build combines product taste with frontier engineering depth: LangChain and LangGraph for orchestration, RAG and data engineering for grounded systems, dreaming/simulation loops for exploration, DevOps for deployment and observability, and multi-cloud comfort across GCP, AWS and Azure.
The accelerator is a senior-level agent swarm built with frameworks like OpenCLAW and Hermes. An orchestrator delegates research, coding, review, automation and publishing to specialized sub-agents running in parallel, keeping the human focus on judgment, scope and product quality.
How I work
Principles I build by
Design is not decoration
A model is only useful if a human can actually use it. I obsess over the experience as much as the architecture.
Grounded over confident
I’d rather a system say “I don’t know” than hallucinate. Retrieval, citations and evals keep it honest.
Ship, then observe
Production teaches what demos can’t. I deploy early, measure everything and iterate on real signal.
Leverage through agents
My swarm does the parallel grunt work so I can stay on the judgment calls that actually need a human.
What’s under the hood
Things I can take from zero to production
Agents & Orchestration
Retrieval & Data Engineering
Cloud & DevOps
Product & Frontend