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Siemens creates AI-powered intelligent entities for industrial automation Aug 11, 2025

At Automate 2025 in Detroit, Siemens unveiled an expanded industrial AI portfolio, introducing advanced AI agents that seamlessly integrate with its Industrial Copilot ecosystem. These agents represent a significant advancement, moving beyond passive AI assistants to proactive, autonomous entities capable of executing entire workflows without human intervention. Siemens’ AI architecture includes a sophisticated orchestrator module that deploys specialized tools to solve complex tasks across the industrial value chain. These agents combine intelligence and autonomy, learning continuously to optimize task execution, while maintaining the flexibility to use external tools and collaborate with other agents. Users retain control, deciding which tasks to delegate to AI agents.


Rainer Brehm, CEO of Siemens Digital Industries' Factory Automation division, emphasized that these AI agents transform industrial operations by enabling systems that independently handle complete workflows. "Through the autonomous iteration of automation technology, we aim to boost productivity by up to 50%, reshaping industrial operations and unlocking new development opportunities," he said.


Automated Autonomous Iteration: How the AI Agent Architecture Works

Siemens' approach separates the user interface (Industrial Copilot) from AI agents providing technical support in the background. The company is also developing digital agents integrated with physical agents, such as mobile robots, to create a fully connected multi-agent ecosystem. This approach enhances interoperability, allowing Siemens’ agents to collaborate with third-party agents.
To further drive innovation, Siemens plans to establish an Industrial AI Agent Trading Center in its Xcelerator online store, enabling customers to access both Siemens-developed and third-party AI resources.


Fully Functional Siemens Industrial Copilot

The Siemens Industrial Copilot, powered by AI agents, covers all stages of the industrial value chain across both process and discrete industries:
Design Copilot: Integrated with NX CAD software, this tool accelerates product design, helping engineers handle complex data, assess trade-offs, and complete cross-domain tasks. It supports natural language interactions, enhancing product development efficiency. Siemens is also developing the Hydrogen Configurator to design hydrogen production plants, generating precise process flow diagrams.
Planning Copilot: In pre-release, this solution uses generative AI to optimize production planning, resource allocation, and scheduling, improving operational efficiency and minimizing waste.
Engineering Copilot: Compatible with the TIA Portal, this tool, launching in 2025, allows engineers to generate automation code using natural language input, improving efficiency and reducing programming errors.
P&ID Digitalization Copilot: This AI-assisted service converts traditional P&ID drawings into digital formats, enabling better integration and management in process industries.
Operations Copilot: Deployed on the Insights Hub platform, it provides real-time operational insights. A new version for shop floor workers is expected by 2025, allowing operators and technicians to query equipment data, diagnose issues, and access solutions through natural language.
Maintenance Copilot: Powered by Senseye, this solution supports the entire equipment lifecycle, from reactive to predictive and preventive maintenance, reducing reactive maintenance time by 25%.


Siemens' Industrial Copilot has delivered impressive results in its own factories and with global customers. ThyssenKrupp Automation Engineering is implementing the technology worldwide, reporting improvements in code quality and development speed. In Bad Neustadt, Germany, the Insights Hub Production Copilot is transforming manufacturing operations by turning data into actionable insights.


Brehm concluded, "Our Industrial AI agents will automate routine tasks, freeing human resources to focus on innovation and complex problem-solving, bridging the skills gap in manufacturing and making Industrial AI accessible to all workers."


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