How AI Streamlines Engineering Problem Solving

🚀 What happens when artificial intelligence starts programming PLCs? We don’t need to imagine it anymore — Beckhoff’s AI CoAgent is already doing it. It’s not just a chatbot. It’s a full AI assistant that understands your automation project: 🧠 Generates TwinCAT PLC code from plain English 🔌 Configures I/O and fieldbus setups 📺 Designs HMI pages from rough sketches 📚 Uses Beckhoff’s internal documentation and your existing project structure 💡 And it’s already used by global leaders: ✅ BMW Group – streamlining PLC coding for production line changes, testing logic, and HMI updates. CoAgent helps engineering teams reduce downtime when switching car models — with automated test sequences and clean documentation. ✅ Oceaneering Mobile Robotics Robotics – programming logic for a fleet of 1,700+ AMRs. Engineers describe scenarios like “two AGVs meet in a narrow corridor” and CoAgent writes the traffic coordination code. It also assists in EtherCAT mapping and diagnostic analysis. ✅ Malisko Engineering, Inc. Engineering (USA) – preserving and scaling expert knowledge as senior engineers retire. CoAgent helps junior engineers create high-quality automation logic faster — accelerating delivery for food, beverage, and pharma clients. ✅ Schirmer Maschinen GmbH Maschinen (Germany) – combining Beckhoff’s IP67 MX-System with CoAgent to build window profile production machines. Engineers use natural language prompts to generate machine logic and HMI — cutting setup time and simplifying commissioning. 📉 Less time programming 📈 Fewer human errors 🧰 More creativity and scalability 💬 All through conversation This is not about AI replacing engineers — it's about engineers becoming 10x more powerful by using AI. 🛠️ The ones who do will lead the future of industrial automation.

Ever wonder how AI agents solve problems one step at a time? 🤔 🔧 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Traditional AI assistants often stumble on complex, multi-step issues – they might give a partial answer, hallucinate facts that don't exist, deliver less accurate results, or miss a crucial step. 🧠 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Agentic AI systems with 𝘀𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 to handle complexity by dividing the problem into ordered steps, assigning each to the most relevant expert agent. This structured handoff improves accuracy, minimizes hallucination, and ensures each step logically builds on the last. 📐𝗖𝗼𝗿𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: By focusing on one task at a time, each agent produces a reliable result that feeds into the next—reducing surprises and increasing traceability. ⚙️ 𝗞𝗲𝘆 𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀 • Breaks complex problems into sub-tasks • Solves step-by-step, no skipped logic • Adapts tools or APIs at each stage 🚦𝗔𝗻𝗮𝗹𝗼𝗴𝘆: - Think of a detective solving a case: they gather clues, then interview witnesses, then piece together the story, step by step. No jumping to the conclusion without doing the groundwork. 💬 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 - 𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘚𝘤𝘦𝘯𝘢𝘳𝘪𝘰: A user contacts an AI-driven support agent saying, “My internet is down.” A one-shot chatbot might give a generic reply or an irrelevant help article. In contrast, a sequential-processing support AI will tackle this systematically: it asks if other devices are connected → then pings the router → then checks the service outage API → then walks the user through resetting the modem. Each step rules out causes until the issue is pinpointed (say, an outage in the area). This real-world approach mirrors how a human support technician thinks, resulting in far higher resolution rates and user satisfaction. 🏭 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 - 𝘐𝘛 𝘛𝘳𝘰𝘶𝘣𝘭𝘦𝘴𝘩𝘰𝘰𝘵𝘪𝘯𝘨: Tech companies are embedding sequential agents in IT helpdesk systems. For instance, to resolve a cybersecurity alert, an AI agent might sequentially: verify the alert details → isolate affected systems → scan for known malware signatures → quarantine suspicious files → document the incident. 📋 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 ✅ Great for complex problems that can be broken into smaller steps. ✅ Useful when you need an explanation or audit trail of how a decision was made. ✅ When workflows involve multiple dependencies that must be followed in a defined order. ❌ Inefficient for tasks that could be done concurrently to save time. ❌ Overkill for simple tasks where a direct one-shot solution works fine. #AI #SRE #AgenticLearningSeries

AI Tools don't replace expertise - they amplify it. Think of AI as a power-tool in the hands of a craftsman: it doesn't make you the craftsman, but it magnifies what you can do when you already know your trade. This elevates your use of Cursor, Claude Code, Gemini CLI and others. First, it's worth recognising what expertise brings to the table: domain knowledge, pattern-recognition, judgement, trade-offs, system-thinking. When you're an engineer who has internalised core concepts - performance, scalability, reliability - you're not just running commands or following recipes. You're asking good questions, choosing the right abstractions, understanding context. AI tools alone don't understand context the way you do; they can generate options and surface patterns, but you still decide which path makes sense. Second, when your expertise is strong, you can leverage AI tools much more effectively. If you know how to frame a problem, break it into sub-problems, assess options, apply constraints, test and iterate, then the AI becomes a multiplier. For example, in some cases AI is used to code, reduce repetitive work, explore large design spaces. But in order to exploit that speed you still need the skill to interpret the results, catch edge-cases, know when to trust the output and when to probe deeper. Third, the trajectory isn't "tools will replace engineers" entirely but rather "tools will raise the ceiling of what engineers can do". AI frees us to focus on higher-level tasks rather than repetitive ones. So the message is: ramp your core engineering capabilities - architecture thinking, domain fluency, product impact - and then use AI to accelerate your reach and explore more ambitious outcomes. Fourth, there's another dimension: the richer your skillset, the better feedback you can give the AI, and the better the AI becomes as a partner. If you are good at prompt-design (and more recently context engineering), good at crafting the right constraints, good at validating and refining outputs, then the AI contributes more. If instead you treat it like a black-box oracle, you risk mis-use or over-dependence. In engineering contexts, guardrails, interpretation and a critical eye remain vital. In short: expertise is the foundation. AI tools are the amplifier. The stronger the foundation, the louder the amplifier becomes. When you bring the skill, the judgment, the systems-level perspective, you unlock far more than you would by simply running the latest tool or model in isolation. #ai #programming #softwareengineering

In our NSF funded Engineering Research Visioning Alliance (ERVA) report on AI and Engineering, we articulated a vision of how AI and engineering can enhance each other in a mutually reinforcing cycle. Among our recommendations was the idea of how AI can transform the entire design to manufacturing to operations ecosystem. This morning I came across PhysicsX, a London based startup. Their solution, quoted below, is a perfect exemplar of the great progress that is likely to occur in the coming months and years: "We empower engineers with AI-driven, real-time multiphysics simulations, enabling faster iterations and automated optimization. Using generative methods, we explore designs previously out of reach, continuously learning from real-world data to enhance accuracy. Our platform serves as a central hub, integrating all stages of the product lifecycle to drive innovation from concept to in-service operations, boosting efficiency, accelerating development cycles, and maximizing performance."

I built an entire PCB from scratch in 35 minutes using AI. FLUX.AI COPILOT transformed my engineering workflow. Starting point: A student's basic block diagram End result: Complete schematic + 3D layout The AI asked intelligent questions: "Which USB serial IC - CH340G or FT232RL?" "Internal or external oscillator?" "Do you want an RC filter on your inputs?" Real engineering decisions while AI handled the grunt work. Technical breakdown: POWER SUPPLY • Generated optimal rail voltages • Added protection circuitry • Selected efficient regulators MICROCONTROLLER • Automated pin assignments • Optimized peripheral routing • Generated decoupling network COMMUNICATION • USB serial interface • I2C expansion ports • Debug headers placement SENSORS • Light-dependent resistors • Temperature monitoring • Motion detection The most powerful part for me were these: AI suggested improvements I wouldn't consider if I were a beginner or even intermediate, but are common for advanced design: • Better ground plane distribution • Reduced EMI through strategic routing • Thermal optimization via component placement This tool cuts my design time by 80%. Engineering evolves. Tools improve. We adapt or fall behind. I've documented the entire process in a free roadmap video. I'll share it with anyone who comments below. Serious about accelerating your PCB design workflow? Drop "Flux" in the comments. Like this post if you believe AI assistants will revolutionize hardware design - or at least make it A LOT easier, faster and more accurate.

Where does #AIinWork fit into the semiconductor engineering landscape? AI is changing not only what we develop in semiconductors but how we develop chips at a very fundamental level. As chipmakers face compounding complexities - performance, efficiency, time-to-market, and R&D costs - traditional workflows are no longer sufficient. We must embrace a new paradigm where collaboration between humans and AI becomes the norm. We call this reengineering engineering. Agentic AI promises to be a genuine force multiplier combining AI speed with human ingenuity. Agentic AI agents will automate many routine but essential tasks - such as linting code or catching signal and clock tree violations - allowing engineers to focus on complex problem-solving and advanced designs. My call to action for engineers is: Adapt or Fall Behind. I talk more about this in my new article in EE Times | Electronic Engineering Times. #AIinWork Synopsys Inc https://lnkd.in/g-Caxq8s