Production

I was stress-testing a RAG system built for regulated industries — financial services and life sciences. The grounding was fine. No hallucinations. What I found were subtler failures — the kind that only surface when analysts run the same query twice, compare citations across sessions, and need to explain to a regulator exactly which document an answer came from. In regulated environments, that’s the standard. And the system wasn’t meeting it. ...

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.” ...

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. ...