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How Agentic AI & TwinCAT CoAgent Are Advancing Industrial Automation Efficiency
Agentic AI with TwinCAT CoAgent boosts automation efficiency by speeding engineering workflows, enabling autonomous system updates, and improving real-time production insight.
www.beckhoff.com
In a workshop at AWS re:Invent 2025, Amazon Web Services will be using Beckhoff’s TwinCAT CoAgent AI assistant as an example to illustrate how agentic AI and cloud technologies optimize engineering and operation for automation systems.
AI-Driven Industrial Automation for Engineering, Operations, and Distributed Systems
At AWS re:Invent 2025 in Las Vegas, attendees will explore how agentic AI is redefining industrial automation across engineering environments, operational workflows, and distributed production systems. A key highlight is the “Build your own AI-driven Industrial Automation System with Agentic AI” workshop, where AWS demonstrates the capabilities of Beckhoff’s TwinCAT CoAgent as an autonomous, AI-based assistant within the industrial production lifecycle.
Why Agentic AI Matters in Industrial Production
Agentic AI is emerging as a differentiator in industrial automation because it enables decision-making that goes beyond classic rule-based control. Instead of relying solely on predefined logic, systems equipped with agent-based intelligence can interpret engineering data, understand system context, and respond autonomously. This shift positions TwinCAT CoAgent as an advanced alternative to traditional engineering tools, offering manufacturers faster adaptation, deeper system insight, and streamlined production changes.
Building Autonomous Automation Systems in Practice
During the workshop, participants construct an agent-driven automation workflow using TwinCAT CoAgent, MCP (Model Context Protocol), and AWS technologies. The exercises demonstrate how AI can interpret piping and instrumentation diagrams and automatically generate TwinCAT 3 code, significantly accelerating engineering processes. The setup is extended with AWS IoT SiteWise to establish real-time connectivity between cloud environments and PLCs, enabling live data handling and automated updates to engineering diagrams, PLC logic, and hardware configurations.
This hands-on approach shows how agentic AI can unify design, simulation, and operation into a continuous loop, reducing engineering time and increasing system reliability.
What Sets TwinCAT CoAgent Apart
TwinCAT CoAgent stands out due to its open architecture and its ability to integrate both established AI models and customer-specific developments. In engineering environments, it supports code generation, debugging, optimization, HMI creation, and documentation. In operations, it extends these advantages to runtime environments through AI-assisted diagnostics and a voice-based interface that helps operators access system information quickly and intuitively.
By combining these capabilities with AWS cloud services, CoAgent offers a level of flexibility, speed, and intelligence that traditional automation systems cannot match. The workshop at re:Invent 2025 illustrates how this integration provides a scalable path toward autonomous industrial systems and more efficient lifecycle management.
www.beckhoff.com
AI-Driven Industrial Automation for Engineering, Operations, and Distributed Systems
At AWS re:Invent 2025 in Las Vegas, attendees will explore how agentic AI is redefining industrial automation across engineering environments, operational workflows, and distributed production systems. A key highlight is the “Build your own AI-driven Industrial Automation System with Agentic AI” workshop, where AWS demonstrates the capabilities of Beckhoff’s TwinCAT CoAgent as an autonomous, AI-based assistant within the industrial production lifecycle.
Why Agentic AI Matters in Industrial Production
Agentic AI is emerging as a differentiator in industrial automation because it enables decision-making that goes beyond classic rule-based control. Instead of relying solely on predefined logic, systems equipped with agent-based intelligence can interpret engineering data, understand system context, and respond autonomously. This shift positions TwinCAT CoAgent as an advanced alternative to traditional engineering tools, offering manufacturers faster adaptation, deeper system insight, and streamlined production changes.
Building Autonomous Automation Systems in Practice
During the workshop, participants construct an agent-driven automation workflow using TwinCAT CoAgent, MCP (Model Context Protocol), and AWS technologies. The exercises demonstrate how AI can interpret piping and instrumentation diagrams and automatically generate TwinCAT 3 code, significantly accelerating engineering processes. The setup is extended with AWS IoT SiteWise to establish real-time connectivity between cloud environments and PLCs, enabling live data handling and automated updates to engineering diagrams, PLC logic, and hardware configurations.
This hands-on approach shows how agentic AI can unify design, simulation, and operation into a continuous loop, reducing engineering time and increasing system reliability.
What Sets TwinCAT CoAgent Apart
TwinCAT CoAgent stands out due to its open architecture and its ability to integrate both established AI models and customer-specific developments. In engineering environments, it supports code generation, debugging, optimization, HMI creation, and documentation. In operations, it extends these advantages to runtime environments through AI-assisted diagnostics and a voice-based interface that helps operators access system information quickly and intuitively.
By combining these capabilities with AWS cloud services, CoAgent offers a level of flexibility, speed, and intelligence that traditional automation systems cannot match. The workshop at re:Invent 2025 illustrates how this integration provides a scalable path toward autonomous industrial systems and more efficient lifecycle management.
www.beckhoff.com
