Agentic-Ai

MCP in Production · Part 1 of 2 Part 2: Authentication, Observability, and Operational Design → Most MCP client examples open a session, call a tool, and close the session. That pattern is fine for demos. It breaks in production in ways that aren’t obvious until you’re staring at a hung process or a spike in latency. This is Part 1 of a two-part series on what it takes to run an MCP client reliably. I’ll cover the transport layer: sessions, pooling, dead connection recovery, timeouts, and the heartbeat. Part 2 covers the system layer: authentication, observability, and operational design. ...

MCP in Production · Part 2 of 2 ← Part 1: Persistent Sessions, Pooling, and Fault Tolerance Part 1 covered the transport layer — keeping sessions alive, recovering from failures, and a few edge cases that only surface when you’re running a real pool under real failure conditions. This part covers what I’d call system readiness: the things that separate a working prototype from something I could hand to a client and say “deploy this.” ...

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. ...