About
The engineer behind the Playbook.
Everything on this site comes out of building AI systems that have to keep working after the demo. This page says who is writing, what I build, and how I work.
What I build
With a background spanning data science, machine learning, R&D leadership, and backend engineering, I bridge the gap between AI research and production reality. I architect autonomous multi-agent systems and agentic AI workflows that solve complex tasks through intelligent orchestration and coordination.
Using the Model Context Protocol (MCP) and frameworks like LangGraph and CrewAI, I build stateful, resilient systems that move beyond prototypes — from workflow automation and data pipelines to full production deployments, all running on scalable Python and FastAPI infrastructure.
Technical stack & expertise
Multi-Agent Orchestration · RAG & Vector Architectures · Data Science & Machine Learning · Production Backend · MCP & Tool Design · Goal-Driven Automation & RPA
How I work
Production first
A technique earns its place here once it has survived a real workload — not because a paper or a vendor page recommends it.
Failure modes named
Every lesson states what breaks and why. A method with no documented failure mode has not been used enough.
Sources, not claims
Each lesson closes with the primary sources behind it, so any statement can be traced to its origin.
Contact
Got a system to build?
If you're building a RAG pipeline, an AI agent workflow, or an MCP integration and want to compare notes, swap ideas, or just avoid the mistakes I've already made — reach out.
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