u/MukundOhmlab

I’m curious how people working with PLCs and industrial control systems are actually using tools like Claude, ChatGPT, Copilot, Cursor or similar coding agents today.

Do you use them at all?

If you do, what are they genuinely useful for? Structured Text, ladder logic, HMI or SCADA work, documentation, debugging, generating function blocks, interpreting manuals, test code, or something else?

If you do not use them, why not? Is it because the code is not good enough, they lack enough context about the machine, the existing tools are faster, security policies prevent it, or you simply would not trust generated code on real equipment?

I’m especially interested in where they start to fall short once you move beyond writing code and get onto the actual machine.

For example, are they useful for generating an initial program but much less useful when you get into live I/O, interlocks, commissioning, diagnosing hardware, understanding why a sequence is not behaving properly, or making changes on site?

Would you ever be comfortable giving an AI agent access to a PLC so it could read the live machine state, write or modify control logic, test the behaviour and see whether it actually worked?

If not, what would stop you?

And if something like that ever was useful, what would you need around the AI before you trusted it? Simulation, strict limits on what it could change, approval before downloads, automatic testing, physical feedback from the machine, something else?

I’m mainly trying to understand which parts of PLC programming and commissioning AI can already help with, and which parts still depend heavily on an experienced controls engineer being there with the machine.

I am trying to understand how people working on real robotic systems are actually using tools such as Claude, Claude Code, Cursor, Copilot or similar AI coding tools today.

If you use them, what are they genuinely useful for? For example, writing ROS code, drivers, perception code, control logic, debugging, test generation, documentation, or something else?

More importantly, where do they start to fall short?

I am particularly interested in what changes once the software has to interact with a real robot. Do you trust AI generated code but still manually handle calibration, hardware integration, testing and commissioning? Would you ever let an agent run code on the robot, observe what happened and modify its own program, or is that a line you would not currently cross?

If you do not use AI tools at all for robotics, I would also be interested in why. Is it code quality, lack of robotics context, safety, reliability, difficulty giving the model access to the real system, or simply that the existing workflow is already better?

Would be especially interested to hear from people working on robots professionally.