The promise of AI agents in manufacturing is about creating systems that can actually adapt when your supply chain gets disrupted, when a machine fails, or when customer demand shifts overnight. But here's the problem: without a clear framework, you end up with AI pilots across different parts of the plant, each solving local problems, none of them working together. A collection of disconnected bots, overlapping efforts, and a governance nightmare.
Gilad Langer, Head of Digital Manufacturing Practice at Tulip Interfaces, has spent 30 years, starting with his PhD research in the 1990s on multi-agent systems, working on this exact problem. His recent framework for Composable Agentic AI in Manufacturing Operations offers a fundamentally different approach to data architecture and governance. More importantly, it provides a practical path forward for organizations trapped between their legacy systems and the promise of AI-driven operations.
Why Manufacturing Needs An Agentic AI Framework
Manufacturing operations are what systems scientists call "complex adaptive systems", they share more in common with traffic patterns and weather systems than they do with customer service chatbots. These systems are inherently chaotic, but not in a bad way. They have patterns, and those patterns can be influenced.
Think about the Toyota Production System. Toyota figured out decades ago that manufacturing behaves like a complex system. Their insight? Don't try to control everything from the top down. Instead, create simple rules that prevent the system from spiraling into bad patterns. Pull instead of push to reduce bottlenecks. Remove obstacles immediately through on-demand problem solving. Create flow rather than fighting against the natural dynamics of the system.
This matters because AI agents work the same way. Each agent is a discrete entity following its own goals, working autonomously but interacting with others. When you put multiple agents together, you get another complex adaptive system. And here's where it gets interesting: if you use a complex adaptive system (your AI agents) to manage a complex adaptive system (your manufacturing operations), you can get the best of both worlds—adaptability plus control.
But only if you have the right framework.
A Data Architecture for AI Agents in Manufacturing
Before you can deploy agents effectively, you need to solve a fundamental data problem. Traditional manufacturing data models are too complicated. They try to capture everything, the physical objects, the transactions, the relationships, the history, all in rigid database structures that require a data scientist to interpret.
The Artifact Model takes a different approach. Walk into any manufacturing facility and ask: what do we actually have here? You'll get a surprisingly short list:
Physical artifacts: machines, tools, rooms, areas, materials, work-in-progress, finished products. Things you can touch.
Operational artifacts: orders, defects, tasks, events, schedules. Things you do with or to the physical stuff.
That's it. Every manufacturing plant, regardless of industry, operates with roughly 10-12 types of artifacts. A CNC machine and a testing device? They're 80% the same from a data perspective. Different specific attributes, sure, but the core structure is identical.
When your operators, engineers, and agents can all look at the same data structure and immediately understand what they're seeing, you've solved the democratization problem. No more waiting weeks for someone to write a custom query or generate a report. The complexity of your data model should never exceed the complexity of what you're actually making.
This means your agents have a shared vocabulary. A machine agent knows how to find its maintenance history. A product agent can query its quality parameters. A schedule agent understands which resources are available. They're all working from the same playbook.
That's it. Every manufacturing operation, rega
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