Obot an AI MCP Gateway: Security, Auth & LLM Integration

Are your AI agents running without guardrails? This hands-on course teaches you to build and harden a production-grade MCP (Model Context Protocol) Gateway — the central control plane that connects your LLMs to tools, APIs, and skills — safely and securely.

You'll start with the why: understanding the architectural problem an MCP gateway solves before writing a single line of config. Most courses throw you straight into installation — this one doesn't. You'll learn why ungoverned AI agents create real security and operational risks, and why a centralised gateway is the right architectural answer.

Then you'll install, secure, and extend your own gateway step by step — from authentication and LLM wiring to content filters, MCP server registration, and a custom skills registry. You'll connect real LLMs, register MCP servers, define your own skills, manage connected devices, and finish by fully hardening your gateway against the threats production environments actually face.

By the end, you'll have a fully hardened AI gateway you can deploy in real projects — not a toy demo, but a working control plane you built and understand from the ground up.

What makes this course different: — Problem-first teaching: every module starts with the real-world challenge before the solution — Security-throughout approach: hardening isn't bolted on at the end — it's built in from Module 1 — Practical, deployable output: you leave with a working, production-ready gateway

What you'll build, module by module: —

Module 1: Understand why an MCP gateway exists and the risks it solves

Module 2: Install your gateway and get it running

Module 3: Lock down access with proper authentication

Module 4: Wire in your LLM

Module 5: Register and connect MCP servers

Module 6: Add content filters to control what flows in and out

Module 7: Build your own custom skills registry

Module 8: Manage connected devices at scale

Module 9: Harden the entire stack for production

Who this is for: AI engineers, backend developers, and platform teams building LLM-powered products who need to go beyond the demo and into production.