Agents

Over the last year, I’ve been building production-grade agentic AI systems — LangGraph state machines, multi-agent orchestration, deterministic validation pipelines designed for regulated environments. And somewhere in that work, I noticed something: the architecture I was using to build reliable AI agents was a pretty accurate model of how I actually operate professionally. So I mapped it out. Not as a second brain or a structured resume. As an agent specification — a design exercise in making professional expertise explicit, structured, and transferable. ...

When I designed the architecture for my KYC onboarding orchestrator, I made a deliberate choice: use MCP not as an LLM-to-tool protocol — the way it was originally designed — but as a service-to-service protocol between a LangGraph orchestrator and a set of independently deployable integration servers. It worked. But it came with real tradeoffs I want to document, because I don’t think this pattern is well understood yet. Background: What I Built The system onboards corporate clients through a fixed sequence of checks — entity profile retrieval, credit rating, sanctions screening, PEP check, CRM update, document generation. Each of those integrations runs as a separate MCP server. A LangGraph graph orchestrates the sequence by calling MCP tools directly from its nodes. ...

I’ve spent the last several months building agentic AI systems — not demoing them, building them. And I want to share something that took me longer than I’d like to admit to fully internalize. The hype is real. The gap is also real. And the gap is closing — but not in the way most people think. This reflects where I am in March 2026, building on roughly 18 months of hands-on agentic work. The field is moving fast and I expect some of this to age. ...