Siemens reduced engineering time by 5x: how an AI agent automated controller programming in battery manufacturing

Siemens engineers working in battery manufacturing used to spend weeks understanding project architecture and writing code for programmable logic controllers (PLCs). After implementing an AI agent that generates code from natural language descriptions, this task now takes days, and engineering efficiency has increased by 50%. Work speed has accelerated by 2-5 times.

Battery manufacturing is a high-tech industry where every second of downtime means millions of dollars in losses. Yet, even here, vast resources are spent on manual programming, debugging, and adapting automation systems. The shortage of skilled engineers and the increasing complexity of production lines all slow down scaling. But there is a way to alleviate this burden and accelerate the adoption of new technologies.

The Problem: Weeks of Manual Programming and Engineer Shortage

Korean battery manufacturers like LG Energy Solution, SK On, and Samsung SDI are actively investing in new technologies and expanding production worldwide. However, this growth faces two significant challenges: the increasing complexity of manufacturing processes and a severe shortage of qualified automation engineers.

Traditionally, each new project or change in the production line required engineers to delve deep into project documentation, spend weeks studying the architecture, and then manually write and debug code for PLCs and human-machine interfaces (HMIs). This process was slow, labor-intensive, and prone to errors, which delayed the launch of new capacities and adaptation to changing market demands.

From SCADA and DCS to AI Agents: The Evolution of Automation

Siemens has long collaborated with Korean battery manufacturers, supplying them with SCADA systems for monitoring and controlling equipment, as well as PCS 7 distributed control systems (DCS) for precise control of production processes, from raw material input to finished product output. These solutions effectively automated physical processes but did not address the problem at the engineering level—the creation of the automation systems themselves.

With the advent of AI agents, it became clear that not only production lines but also their design and programming processes could be automated. A tool was needed that could understand engineering tasks not as rigid scripts but as natural language descriptions, and then generate ready-made solutions based on them.

How the AI Agent for Engineering Was Designed

Siemens developed a specialized AI agent designed for automating engineering in industrial environments. The key difference from general language models is its deep understanding of industrial process specifics, project architectures, component relationships, and customer standards.

The agent was designed to perform several core functions:

• PLC Code Generation. An engineer describes the manufacturing process in natural language (including Korean), and the agent automatically generates the corresponding code for programmable logic controllers.
• HMI Screen Creation. Based on described requirements, the agent generates human-machine interface screens used by operators to monitor and control equipment.
• Accelerated Onboarding. New engineers, who previously needed weeks to understand project structures, can now grasp them in days with the agent's assistance.

The AI agent is integrated with the Siemens TIA Portal engineering platform, allowing it to operate directly within the engineers' familiar environment.

Implementation: From Pilots to Widespread Use

The implementation of the AI agent began with pilot projects within Siemens' own factories and then extended to partners in Korea. Gradually, engineers began to trust the system, seeing how it reduced routine work and minimized errors. The main focus was on training employees to effectively interact with the agent, transforming it from a simple tool into a full-fledged assistant.

This approach allowed for smooth integration of the new technology into existing workflows without drastic overhauls or resistance from personnel.

Results of AI Agent Implementation

The implementation of the AI agent allowed Siemens and its partners to significantly reduce engineering time, improve its quality, and decrease reliance on scarce highly skilled specialists. This provided a competitive advantage in the rapidly growing and demanding battery manufacturing industry.

How to Implement This in Your Business

If your company faces a shortage of qualified engineers, high design complexity, or the need to rapidly scale manufacturing processes, an AI agent can be a solution:

• Start with automating routine coding. Identify repetitive tasks for writing PLC or HMI code that consume a lot of time. An AI agent can take these on, freeing engineers for more complex challenges.
• Integrate AI into existing tools. To ensure rapid adoption, embed the agent into the software environments your engineers already use.
• Use AI for training and onboarding. The agent can become a powerful tool for accelerating the training of new specialists, providing them with quick access to project structures and best practices.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager