The Intelligence Stack: Engineering Production-Grade Agentic AI Systems

The Intelligence Stack: Engineering Production-Grade Agentic AI Systems If you've ever built an AI-powered feature that worked beautifully in a demo and then quietly became your biggest infrastructure bill in production — this one's for you. Scaling agentic AI isn't just about picking the right model. It's about building the right system around the model: routing requests intelligently, compressing knowledge into smaller deployable artifacts, retrieving the right context at the right time, serving efficiently under load, and keeping the whole thing honest, safe, and observable. That's a lot of moving parts — and most teams learn them the hard way. This guide is a practical systems deep-dive into every layer of that stack. Whether you're an ML engineer trying to cut inference costs, a platform architect designing a multi-agent workflow, or a technical lead trying to understand where things go wrong at scale — there's something here for you. Let's get into it. TL;DR Running agentic AI at