AI System Design: LLM Gateways, RAG & Agent Platforms

About this course

AI Systems Architecture: Design, Scale, Secure, and Optimize LLM Platforms

Imagine you're the architect, and your design review is tomorrow morning. Your diagram has a box labelled “LLM” — and no numbers.

This course is about everything that box hides.

The model is the least stable component in your system. Design around it.

Model latency, quality, pricing, and availability can all change. Your architecture must remain correct, safe, fast, and affordable despite those changes — and you must be able to prove that it works.

Build an AI Platform You Can Actually Test

Throughout this course, you'll work on Polaris, a fictional enterprise's shared AI platform.

Polaris runs on your laptop, with no GPU or paid API required. A deterministic model simulator stands in for real AI providers, making the results reproducible and the exercises free to run.

You won't just discuss architectural principles. You'll verify them using fitness functions — automated tests that run the platform against defined workloads and fault scenarios, then fail when the architecture violates its requirements.

The goal is simple: move beyond diagrams and assumptions to measurable, testable AI systems.

What You'll Design, Build, and Prove1. Requirements, Capacity Planning, and Economics

Decide when you need an LLM — and when you don't.

Define measurable quality requirements, latency budgets, and cost per successful outcome.

Estimate capacity, traffic, concurrency, and operating costs before committing to an architecture.2. The Enterprise AI Reference Architecture

Design the complete request path from the edge to models, retrieval systems, and tools.

Integrate AI workloads with existing enterprise message brokers and event streams.

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