Agentic AI for Manufacturing Build with LangGraph & MCP

What you'll learn

---

Build 4 progressively intelligent AI agents in Python and LangGraph that observe, diagnose, advise, and act on live industrial plant data.
Connect LLM agents to a live Unified Namespace using MCP - giving your agent real-time access to MQTT, Sparkplug B, and OPC UA plant data.
Design a human-approval gate so your agent can recommend action safely - without ever making an uncontrolled change to a live process.
Apply ISA-95 context and namespace structure so your agent reasons accurately instead of just reacting to raw, meaningless sensor numbers.
Requirements

---

Basic Python is helpful but not mandatory - every line of code is explained as it is written, and you can follow along even if you have never coded an AI agent before. No prior AI or LLM experience required. If you can read a sensor reading and understand why a number matters on a plant floor, you already have the instinct this course builds on. Completing "MQTT, OPC UA & Sparkplug B: Build a Unified Namespace" (Course 1) first is strongly recommended - this course builds directly on the HiveMQ, Ignition Edge, InfluxDB, and Grafana stack from that course. If you are new to the Unified Namespace, a short Bonus section at the end of this course will get your stack running from scratch in under an hour. You will need a Windows or Mac laptop capable of running Python and a free-tier LLM API key - total API cost to complete every exercise in this course is typically under five dollars. No OT or industrial automation background required, though if you have it, you will recognise every example in this course from your own plant floor.
Description

---

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 Loop
Most 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 Build
Using 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 production-grade industrial AI agent - observe, reason, recommend, act, and the human-approval
gate that makes the last step safe.
How to use LangGraph to structure an agent's reasoning as a graph, the same way ladder logic structures a PLC's program -
if you have ever read ladder logic, this will feel immediately familiar.
How to use MCP to give an LLM agent controlled, scoped access to a live industrial namespace - not generic API calls, but the
same tool-access pattern increasingly used in production agent architectures.
Why an agent needs ISA-95 context - site, area, line, equipment - to reason accurately, and what happens when it does not have that structure.
How to design a human-in-the-loop approval architecture that lets an agent recommend action in real time without ever making
an uncontrolled change to a live process - and why that design is not a limitation, but correct engineering.
How to build a full audit trail so every agent decision, every approval, and every action is logged and reviewable.
Who this course is for

---

This course is for anyone who has ever looked at a plant dashboard and wondered why nobody was watching it - and who wants to build the thing that watches. It is for IIoT and OT engineers who have built a Unified Namespace and now want to make it intelligent, not just visible. If you have data flowing through MQTT and Sparkplug B and you are asking "what do I actually do with this," this course answers that question with working code. It is for software developers and AI practitioners who understand LLMs and agents in a general sense, but have never connected one to a real-time industrial system - and want to learn the specific architecture that makes that safe and reliable in a plant environment, not just a chatbot demo. It is for maintenance, reliability, and process engineers who have relied on instinct and twenty-plus years of pattern recognition to catch problems before they become failures - and who want to understand how that instinct can be captured in a system that never sleeps and never retires. It is for solution architects, presales engineers, and technical consultants in Industrial AI, Digital Twin, or Industry 4.0 roles who need to speak fluently about agentic AI architecture - observe, diagnose, advise, act, and human-in-the-loop approval - and who want hands-on proof behind that fluency, not just slide-deck familiarity. This course is not for you if you are looking for a general introduction to ChatGPT prompting, or a theoretical overview of AI without code. Every lecture in this course builds something real, against a live simulated plant, using the same tools running in industrial environments today.
Homepage

---