Agentic AI for Manufacturing: Build with LangGraph & MCP

About this course

This course contains the use of artificial intelligence.

Build Four AI Agents That Watch a Live Industrial Plant, Diagnose Problems, and Recommend Action — Safely, With a Human Always in the LoopMost agentic AI courses teach you to build agents that book flights or summarize emails. This course teaches you to build agentic AI for manufacturing — where the data is real-time sensor data, the stakes are a live process, and an agent that acts without oversight is not a feature. It is a risk.

You will build four agents, each one smarter than the last, connected to a real Unified Namespace running MQTT, Sparkplug B, and OPC UA — the same protocols running on plant floors today.

Agent 1 — The Observer reads live plant data every 30 seconds and describes what is happening, in plain English.

Agent 2 — The Diagnostician does everything Agent 1 does, and when something looks wrong, reasons about why.

Agent 3 — The Advisor diagnoses the problem and recommends a specific action, with urgency and confidence attached.

Agent 4 — The Supervised Operator routes its own recommendation through a human approval gate before it acts — and logs every decision. Nothing happens in your plant without a human signing off, exactly the way Management of Change already works in your operation today.

What You Will BuildUsing Python, LangGraph, and MCP (Model Context Protocol), you will build each of these four agents from scratch against a realistic simulated manufacturing scenario — Walker Manufacturing, a polymerisation plant where a cooling pump failure develops slowly across a single night shift. By the end of the course, you will have working code for all four agents, plus a Streamlit oversight dashboard where a human approves every recommended action before it executes.

What You Will Learn

The architecture behind every pro

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