（A practical closed-loop control walkthrough using Node-RED, MQTT, and DeepSeek LLM.）
We believe the next generation of industrial systems might not just react — they might think, act, and learn.
Not just retrieving reference data like RAG, offering suggestions like a chatbot, or passively answering questions — but actively executing decisions and closing control loops like a human expert would.
Factory Agent is our attempt to sketch that possibility:
A reasoning LLM agent system, running on Node-RED, listening to real-time MQTT topics, and making decisions for a simulated manufacturing factory
It’s open-source, event-driven, and experimental.
Not because we have all the answers — but because this space is too exciting not to explore.
The industrial world is full of data. But decision-making — especially at the edge — still relies heavily on human intuition and manual rules.
Today’s AI applications in industry often remain passive: they answer questions, summarize documents, or assist with interfaces. But very few actually make decisions, execute commands, and take accountability for outcomes in a closed-loop way.
Our question was simple:
Can we build an agent system that senses factory events, reasons about possible strategies, and executes actions — like a junior operator empowered with expert instincts?
To explore this, we started with a minimal viable system — and we are sharing it open-source to invite the broader community to build, extend, and challenge it.
In classical control theory, a closed-loop controller continuously monitors outputs and feeds information back into the system to adjust behavior — enabling self-correction even under changing conditions (e.g., PID loops in process control).
We extend this paradigm into the LLM-agent world.
The loop now operates across five essential layers:
Perception Layer