Why Industrials Need Multi-Agent AI Systems

Engineers build from modular components, whether it's a factory, a rocket ship, or a bulldozer. It is now known that this same modular, structured approach can be applied to AI, making it easier to understand, test, validate, and deploy. This modularity is particularly important when you consider bringing sensor data into an AI automation workflow. Individual agents in an AI system sense through sensors that take in the information from the simulator or real system, and then they act, outputting decisions about what action to take to control that system. When you build your agent, you can also add in perceptors.

Perceptors use advanced machine learning models to spot patterns and make predictions from sensor information. A platform of intelligent building blocks that engineers can use to create powerful, autonomous systems without needing a background in AI development is desperately needed in industrial and large-scale processes. AI isn't about replacing workers; just like the internet, databases and the cloud, it's a tool that empowers them. By capturing and preserving high-value skills, AI can support the manufacturing resurgence in the U.S. and beyond.