AI agents can do much more than that. They connect existing sensors, machines, ERP systems, and cloud platforms at an increasing number of key points in the industry to form self-learning networks that control, monitor, and optimize production processes in real time.

Even though the idea may seem obvious, an AI agent used in industry is much more than a chatbot equipped with the usual agent gadgets. It follows clear instructions such as “optimize the energy consumption of this line” with strategic measures and executes targeted actions via software interfaces or robot controls. An AI agent also learns continuously from feedback.

Implement AI projects without deep coding

Altair asks the legitimate question “AI agents are putting the smart in smart factory – are you ready?” and shows how AI agents can ensure that companies not only become faster, but also more resilient to disruptions. With RapidMiner, Altair has created a comprehensive platform for artificial intelligence, machine learning, and data analysis. It is the central tool Altair uses to enable AI agents to “think” by analyzing data, learning patterns, and providing recommendations for action that make industrial processes measurably more efficient. Altair reduces the fear of contact with AI agents with its no-code/low-code interface, which creates an intuitive drag-and-drop environment for data scientists and engineers so they can implement AI projects without deep coding.

On the way to fully agent-controlled factories

Siemens, Altair's parent company, is also creating autonomous systems with its new Industrial AI Agents that evaluate production data and implement recommendations for action. According to Siemens, these agents have led to productivity increases of up to 50% and 15% higher plant availability in pilot plants. Together with Microsoft and OpenAI, the capabilities of Industrial AI Agents are to be integrated into the Industrial Copilot environment in order to continue on the path toward fully agent-controlled factories.

AI agents monitor camera data

Pegatron, one of the world's largest electronics manufacturers, relies on visual agents. With the support of NVIDIA Metropolis and Omniverse Digital Twin, AI systems control the assembly processes. Pegatron's AI agents monitor camera data, compare it with the digital twin of production, and automatically intervene in the event of deviations. Here, too, the figures speak for themselves: Pegatron reports 67% fewer errors and 7% lower labor costs per line.

In the automotive sector, BMW has also introduced AI agents for visual surface inspection at its plants in Leipzig and Munich. Image processing agents detect paint defects at high speed and document them in the system. Inspection times are said to have fallen by 30%, while the quality rate has risen.

Complete product ideas at the touch of a button

An adaptive language model with generative design and conceptual know-how – this combination recently earned HARTING the “FREDDIE” Industrial AI Award. According to HARTING, its AI system “Connectivity AI” delivers complete product ideas for new connection solutions at the touch of a button, including sketches, CAD models, and simulations. This is because “Connectivity AI” allows product developers and designers to interact intuitively with artificial intelligence in natural language and exchange requirements and proposed solutions.

Not only economic, but also ecological benefits

However, the use of AI agents is not limited to quality control and increasing production and resilience; resource conservation is also coming into focus. According to its own statements, Schneider Electric uses so-called EcoStruxure AI agents to monitor energy flows in factories. These AI agents adjust load distributions in real time and are said to be able to reduce energy consumption by up to 30%.

ArcelorMittal integrates its AI agents into steel production. According to the company, a combination of machine learning and process data control has enabled it to save 5 to 10% in energy and reduce CO2 emissions at the same time.

Multi-agent systems ensure a balance between costs and output

What James Bond's CIA buddy Felix Leiter is to him, multi-agent systems are to AI agents – networks of specialized agents that communicate with each other. The startup Juna AI, for example, relies on reinforcement learning to optimize multiple production goals simultaneously, including throughput, quality, and energy consumption. In practice, this should result in a self-organizing control system that constantly finds the best possible balance between costs and output. Robotics manufacturers such as FANUC are also researching the use of adaptive agents that independently improve robot movements. Thanks to field and cloud data, they should be able to reduce cycle times by up to 20% and reduce downtime through predictive maintenance.

Natural Intelligence Agency stays on top

The capabilities of autonomous AI agents are already more than impressive, and it doesn't take much imagination to picture what's coming next. The term “autonomous agent” often has negative connotations. So, it'll be crucial that humans continue to play a central role despite increasingly comprehensive automation. In the imaginary Natural Intelligence Agency of industry, AI agents take over data-intensive routines, but human experts continue to monitor decisions, train the systems, and strategically design the processes. Companies such as Siemens and Bosch are therefore investing heavily in training programs to train employees to become “AI operators” – flesh-and-blood specialists who manage AI agents instead of operating machines.