Building a Broker to Learn a Protocol: Andreas Vogler and the Story Behind MonsterMQ

From Telemetry to Intelligence: What Cumulocity’s IIoT Platform Actually Does

(Our topic. Our tone. Sponsored by Cumulocity [https://www.cumulocity.com/?utm_source=website&utm_medium=link&utm_campaign=ITOT_Insider] *) It’s episode 50 (🎉) of the podcast, and we’re only now getting to IIoT platforms. We sat down with Jürgen Krämer [https://www.linkedin.com/in/juergenkraemer/], Chief Product Officer and Managing Director at Cumulocity [https://www.cumulocity.com/?utm_source=website&utm_medium=link&utm_campaign=ITOT_Insider]. Cumulocity is an IIoT platform with currently more than 25 million connected devices, three billion messages processed per day, and a roster of customers that includes wind energy operators, healthcare devices, and crane manufacturers. Jürgen has been in the IoT and analytics space for over 20 years, which means he’s lived through every wave of the hype cycle and that made our conversation another super interesting one! He also knows that the term “IoT” is notoriously elastic: it gets applied to everything from smart lawnmowers to offshore wind farms, and that ambiguity is a genuine problem when you’re trying to make a technology decision. So time to demystify some concepts around (I)IoT Platforms! IIoT Platform vs Historian We’ve written before about the power of the process historian, and in a typical manufacturing or process plant, the historian is genuinely well-suited. It is designed for high-density time-series capture in a controlled, physical environment where you own the network, the devices are close together, and the architecture is well understood. The moment you move to distributed assets out in the world: in customers’ facilities, on wind farms, on construction sites, in buildings, data centers, and so many others… the picture changes entirely. Because in those cases, you don’t own the network. You have no physical access. You’re managing connectivity across 30,000 turbines in a dozen countries. Firmware updates need to happen over the air. Security is paramount because the device is sitting inside someone else’s infrastructure. That is where the IIoT platform becomes the right tool. As Jürgen puts it: “You need to connect and manage devices you don’t control, in environments you’ve never seen, at a scale that makes manual management impossible.” The M2M → IIoT → AIoT evolution Time for a history lesson! The first wave, around 2010, was M2M (machine-to-machine). It was essentially about connectivity: get the device online, manage the firmware, enable remote access. Useful, but narrow. The IIoT era, roughly 2015 to 2024, added the layer above: dashboards, analytics, edge computing, application enablement. Operations became more optimised, but the work was still largely human-centric. An alert would fire, and a technician would spend hours diagnosing the situation (reviewing log files, cross-referencing documentation, forming a hypothesis). AIoT (what Cumulocity now positions itself as) is the next step. The vision is that the agent does the diagnosis. The technician receives a package: probable bearing failure on Pump 4, replacement part ordered, repair guide attached, shutdown recommended at 14:00, awaiting your approval. The human is still in the loop, but the cognitive labour of diagnosis shifts from the person to the system. We’ve used the term “virtual operator” in previous articles. This is what it could look like in practice (obviously given the availability of enough data, context and the right understanding of the physical reality!) The part most people still skip: Context Context is the most important thing to get right today. It’s the answer to scaling [https://itotinsider.com/operational-data-platform/], it’s the answer to UNS [https://itotinsider.com/the-unified-namespace-uns-explained/], it’s a necessity for AI [https://itotinsider.substack.com/p/industrial-ai-unpacked-introducing]. And thus we’d encourage you to slow down here. Every AI initiative in industry eventually runs into the same wall: raw telemetry is not enough. A value arriving every second from a sensor labelled “Reg_004” means nothing to an LLM, and very little to a human who didn’t configure that tag. Feed that data stream to an AI agent without context, and you will get hallucinations. Jürgen’s team has run this experiment directly [https://www.cumulocity.com/blog/grounding-aiot-in-physical-truth-with-cumulocity/?utm_source=website&utm_medium=link&utm_campaign=ITOT_Insider]: same query, without and with a semantic layer. Without it: plausible-looking KPIs that are simply fabricated. With it: accurate, actionable results (take a look at the result in this video [https://www.cumulocity.com/blog/grounding-aiot-in-physical-truth-with-cumulocity/?utm_source=website&utm_medium=link&utm_campaign=ITOT_Insider]). What does context actually mean here? Jürgen describes three layers: * The first is the system of record — the secure, scalable, mission-critical foundation. This is not exciting, but it is load-bearing. You do not rebuild it from scratch. * The second is the semantic layer. This is what makes industrial data AI-ready. It includes the metadata (is this temperature reading in Celsius or Fahrenheit? what are the normal ranges? when was the sensor last replaced?), the alarm history, the maintenance documentation, and critically: the asset hierarchy. This sensor belongs to this component, which is part of this pump, which sits in this production line, in this plant. Without that hierarchy, you cannot roll up to a meaningful OEE calculation. Without that context, the AI agent is just pattern-matching on noise. * The third layer is the agentic layer — where the AI agents operate, with access to the semantic layer as their knowledge base. There is a parallel here worth naming. We’ve argued for years that industrial DataOps — getting data clean, contextualised, and accessible — is foundational work that pays off for humans first and AI second. Jürgen made the same point: companies that invested in a proper semantic layer years ago, for human operators, got a head start. They built the infrastructure that now, with AI on top, is worth considerably more than they probably expected. Should you vibe-code your own IIoT platform? The short answer is no. The longer answer is: it depends what you mean. David raised the question that’s circulating everywhere right now: with AI-assisted development, can’t we just build our own platform? It’s a reasonable thing to ask, given that a motivated developer with a good LLM can now scaffold something that looks functional in a weekend. The problem is in the word “looks.” The system of record layer — the part that manages tens of thousands of devices, handles over-the-air firmware updates, maintains security compliance in a post-NIS2 world, and operates at 24/7 SLAs — is mission-critical infrastructure. Generating a million lines of code with an AI framework and then being responsible for operating it against contractual uptime commitments is not a viable strategy. As David put it: “Who takes responsibility when things go sideways — not just on availability, but on cybersecurity and supply chain risk?” Jürgen’s distinction is worth keeping: AI-assisted development is genuinely useful at the application layer — building custom dashboards, tuning models on your own data, accelerating vertical use case development. It is not a substitute for a proven platform at the foundation. We’ve watched this cycle before. The Excel macro era, the Access database era, the no-code/low-code era — each one produced a generation of fragile, undocumented tools that someone had to maintain long after the person who built them had moved on. AI-assisted development is the new version of this pattern. Some of what gets built will be excellent. Much of it will become technical debt. The strategic question remains the same as it always has: where does your competitive advantage actually live? (Probably not in having built your own secure, scalable data foundation from scratch) Finding the right use case is harder than it looks Cumulocity has seen hundreds of deployments — predictive maintenance, asset performance management, remote service operations, cybersecurity compliance — and the consistent failure mode is enterprises that start by playing with the technology rather than by defining the business outcome. The right starting point is not “what can we do with AI?” It is “where do we get the most leverage from our investment?” Those are different questions, and the second one is harder to answer without experience. Cumulocity is offering a free one-day consulting workshop for organisations that want to identify their best starting point for an AIoT journey. If you prefer to get your hands on the platform directly, there is also a free trial at cumulocity.com [https://www.cumulocity.com/start-your-journey/free-trial/?utm_source=website&utm_medium=link&utm_campaign=ITOT_Insider]. On our side, the workshop assessment framework we use in our own engagements is available at itotinsider.com [https://itotinsider.com/workshop/]. About Cumulocity Cumulocity is the leading independent AIoT platform, built to bridge the gap between IT and OT. We empower equipment manufacturers and distributed asset operators to securely connect, manage, and extract value from millions of devices on a global scale. From remote device management to industrial DataOps and AI-ready semantic models, Cumulocity provides the mission-critical foundation needed to turn raw telemetry into actionable intelligence; and securely close the loop by executing remote commands, updates, and automated actions right back at the edge. Stay Tuned for More! 🙋 Join the [https://itot.academy]ITOT.Academy [https://itot.academy] (new cohort in September) → [https://itot.academy]📘 Pre-order the [https://itotbook.com/]IT/OT Handbook [https://itotbook.com/] (+ claim the bonuses!) → [https://itotbook.com/] Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence. 🚀 See you in the next episode! Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: (*) At the IT/OT Insider we do value our independence and transparency. So as we look for ways to pay the bills we were looking for ways to work with sponsors without giving up on those principles. This is where the idea of sponsors comes from. Together with a few selected sponsors we’ll explore some topics that we both find interesting in the same way we write our normal articles. In the coming weeks you’ll find a couple of pieces that have been sponsored. Feel free to contact us if you are interested in a partnership as well. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Building a Broker to Learn a Protocol: Andreas Vogler and the Story Behind MonsterMQ

Before we start… We started our 4th IT/OT Academy two weeks ago. Last week and this week we’ll talk about architectural typicals for different use cases, dissect vendor diagrams and step into organizational dynamics with our Cooperation Models. All while our students can network and learn from each other. The next Academy kicks off September 18 and our early bird offer is open. Are you interested in joining? Claim your seat early enough, because the first registrations are already in 🙂 🙋 Join the [https://itot.academy]ITOT.Academy [https://itot.academy] (new cohort in [https://itot.academy]September [https://itot.academy]) → [https://itot.academy] And… have you already pre-ordered our IT/OT Handbook? If so, don’t forget to register to receive your exclusive bonuses. 📘 Pre-order the [https://itotbook.com/]IT/OT Handbook now [https://itotbook.com/] (+ claim the bonuses!) → [https://itotbook.com/] Andreas Vogler [https://www.linkedin.com/in/andreas-vogler/] crossed paths with us at Hannover Messe this year. We all know that moment of “I know your face, I know your name”... and a few weeks later, here we are. Andreas is the Chief Innovation Officer at ETM, the Siemens subsidiary behind WinCC Open Architecture, one of the major SCADA systems in industrial automation. Some years ago, Andreas already built a project called Automation Gateway. A colleague once described it as a collection of open source pieces stitched together and called it Frankenstein of Automation. When he wanted to learn more about MQTT, he built his own broker (which we think is pretty awesome 👍) and the name almost wrote itself: Frankenstein’s monster. MonsterMQ. (If you’re new to MQTT and want a solid grounding before diving in, we covered the protocol in depth in our episode with Kudzai Manditereza [https://itotinsider.substack.com/p/mqtt-vs-opc-ua-with-kudzai-manditereza].) From broker to MQTT+ The first version of MonsterMQ had no UI. Configuration files only because that was the fastest way to get it working. Then AI coding arrived. First GitHub Copilot (hat strange experience of typing a line of code and having it suggest the next one) and eventually more capable agentic tools. Suddenly he could build dashboards. He pulled connectivity features from the Automation Gateway: PLC4X integration for direct PLC communication, database backends (PostgreSQL, MongoDB, SQLite), workflow engines and later also AI agents. What MonsterMQ has become today is probably best described as ‘MQTT+’. A broker, but also a connectivity layer, a persistence layer, and increasingly an intelligent edge runtime. Andreas runs it at home. He has photovoltaic panels, a jacuzzi, a collection of sensors, and an agent inside the broker that checks every 30 minutes whether it makes sense to run the jacuzzi heater. “It tells me: now is a good time.” Practical. A little nerdy. Completely in character. Open source in industrial settings MonsterMQ is fully open source and is not a Siemens or ETM product. It’s Andreas’s project. And that’s where it gets interesting for anyone thinking about adopting it in a production environment. Companies come to him, they like what they see, and then they ask: can I get a support contract? The answer right now is no. That’s not a criticism; it’s just the reality of where the project is. Andreas is aware of it. He’s had those conversations. The EU’s Cyber Resilience Act adds another layer [https://itotinsider.substack.com/p/nis2-and-operations-key-takeaways]: if you’re supplying software to European companies, there are obligations around development process, supply chain and security disclosure, just to name a few. This purely open source side project isn’t yet set up to meet those requirements. None of that makes MonsterMQ unsuitable for exploration or for production grade use, as long as you have the needed skills in-house. But if you’re planning to put it at the heart of a production line, go in with your eyes open. Know what you’re taking on. Vibe coding and the architect problem Andreas has been through the full arc of AI coding (or Vibe coding). Early on, he reviewed every line the AI produced: checking that new code landed in the right class, in the right file, that nothing unnecessary was created. These days, for personal tools like a custom MQTT explorer he built, he doesn’t look at the source code at all. “I don’t care. It works.” But for MonsterMQ itself the standard is different. The human has to remain the architect. Someone needs to know what they want, where it belongs, and whether what was generated is actually doing the right thing. AI coding doesn’t remove the need for expertise. It changes where that expertise matters. You need less of it in the execution and more of it in the direction. The ProveIT demo of WinCC Open Architecture with AI-driven engineering (where an engineer can give the system a Modbus device specification and have it configure the driver automatically) shows where this is heading for industrial software. Faster, yes. But the engineer still has to understand what they’re asking for. Contribute or follow along MonsterMQ is on GitHub. Andreas would welcome contributors. He actually met one of them in person at Hannover for the first time, which he described as “a cool experience.” The project has a demo server, a dashboard, and an active development roadmap. What happens next with MonsterMQ, whether it stays purely open source or evolves into something with commercial backing, is genuinely open. Andreas isn’t ruling anything out. Find Andreas on LinkedIn [https://www.linkedin.com/in/andreas-vogler/] and the project at monstermq.com [https://monstermq.com/]. Stay Tuned for More! 🙋 Join the [https://itot.academy]ITOT.Academy [https://itot.academy] (new cohort in September) → [https://itot.academy]📘 Pre-order the [https://itotbook.com/]IT/OT Handbook [https://itotbook.com/] (+ claim the bonuses!) → [https://itotbook.com/] Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence. Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work. 🚀 See you in the next episode! Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

i3X Explained with John Dyck and Jonathan Wise: One API to Connect Them All?

Before we start… We just started our 4th IT/OT Academy last week. The next one kicks off September 18 and our early bird offer is open. Are you interested in joining? Claim your seat early enough, because the first registrations are already in 🙂 🙋 Join the [https://itot.academy]ITOT.Academy [https://itot.academy] (new cohort in [https://itot.academy]September [https://itot.academy]) → [https://itot.academy] And… have you already pre-ordered our IT/OT Handbook? If so, don’t forget to register to receive your exclusive bonuses. 📘 Pre-order the [https://itotbook.com/]IT/OT Handbook now [https://itotbook.com/] (+ claim the bonuses!) → [https://itotbook.com/] Welcome to another episode of the IT/OT Insider Podcast! We sat down to talk about i3X with John Dyck, CEO, and Jonathan Wise, Chief Technology Architect at CESMII. CESMII is a US not-for-profit consortium, federally funded by the Department of Energy, with a clear mission: make smart manufacturing accessible for all manufacturers, not just the ones with deep pockets and dedicated engineering teams. Fifty projects. One persistent frustration. Before CESMII built anything, they watched. Jonathan’s team ran or supported roughly fifty smart manufacturing projects with US manufacturers — small facilities getting their first sensors connected, large enterprises with decades of automation behind them. Across all of them, the same pattern appeared: interoperability was never designed in. Not because anyone was careless. The mindset across much of the industry — especially in the US, as Jonathan puts it — is to find the problem in front of you, fix it, and move on. The result is what he describes as a patchwork quilt: software layers brought in at different times to solve different problems, never built to work together, often arriving through acquisition. You end up with deeply heterogeneous architectures where nobody — and no system — has a shared understanding of the data. Out of those fifty projects, CESMII extracted valuable lessons. Jonathan’s team identified three things that had to be true for manufacturing data to be genuinely usable across systems. They called them the Smart Manufacturing Imperatives. The first two set the foundation. The third is where i3X comes in. The 3 Smart Manufacturing Imperatives The first imperative is about information modelling. Before data can travel, it needs to mean something. CESMII calls this Information Model Standardisation — an open, standards-based approach to describing manufacturing devices, assets and processes consistently. They’ve built this out through their Smart Manufacturing Profiles [https://www.cesmii.org/technology/sm-marketplace/]: a library of reusable, community-maintained models that give manufacturers a head start rather than asking everyone to start from scratch. Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work. The second imperative is about the platform layer that sits on top of those models. A Contextual Manufacturing Information Platform — a clear set of requirements for what any serious industrial data platform needs to support in order to enable application interoperability. If that sounds familiar, it should: it maps closely to what we’ve described in our own Industrial Data Platform Capability Map [https://itotinsider.substack.com/p/industrial-data-platform-capability-map-dataops-uns-v2]. The capabilities required are broadly the same. The language is different, but the thinking converges. The third imperative is having an open and common API to get to all data in context. Even with shared models and capable platforms, applications still can’t talk to each other if every vendor exposes their data through a different API. i3X — the Industrial Information Interoperability eXchange — is CESMII’s answer to that: an open, common API for manufacturing systems that enables rapid application development, AI deployments, Edge AI, and supply chain integration. We’d go further and say it might also be the answer to something we’ve been asking since we published our Capability Map: what does Capability 7, Data Sharing [https://itotinsider.substack.com/i/171464217/7-data-sharing], actually look like in practice? What i3X is i3X is a vendor-agnostic, open API specification that any manufacturing information platform can implement, regardless of what’s running underneath. It is not a platform. It doesn’t tell you how to build your system. It defines the surface your system needs to expose: typed data, live and historical access through a consistent structure, hierarchical organisation that can expand into a full graph of relationships. Explore CESMII’s interactive visualization here: https://i3x.dev/viz/ [https://i3x.dev/viz/] Explore the API endpoints and try them out yourself via https://api.i3x.dev/v1/docs [https://api.i3x.dev/v1/docs] To make all of this even more accessible, CESMII released i3X Explorer: a free test client that lets you load your implementation and see exactly which functions are covered and which aren’t. Whether you’re a developer building a platform or an engineer specifying a project, it gives you a concrete view of where you stand. At the ProveIT event in Dallas, six vendors demonstrated live interoperability against i3X on the same stage, (Aron Semle from HighByte [https://itotinsider.substack.com/p/aron-semle-on-mcp-agents-and-the] who joined us on the podcast a few weeks ago is one of them). Take a look at the full recording here: Why it matters beyond the API. The bigger picture, as John frames it, is democratisation. If the API is common, a developer or an AI model built against i3X works across any compliant platform. That changes the economics entirely — especially for small and medium manufacturers who can’t afford to rebuild integrations for every environment they deploy into. It also opens up genuine innovation: build once, run anywhere, learn fast, iterate. For manufacturers, the practical message is simple. Start asking your vendors whether they support i3X. If they don’t, ask why. The vendor community has shown it’s willing to move when customers ask for it. The big question for the coming years now becomes: Will this one stick? So…. to be continued 🙂 Extra Resources * i3X specification: https://www.i3x.dev [https://www.i3x.dev] * i3X on GitHub: https://github.com/cesmii/i3X [https://github.com/cesmii/i3X] * Our HighByte episode with Aron Semle: https://itotinsider.substack.com/p/aron-semle-on-mcp-agents-and-the [https://itotinsider.substack.com/p/aron-semle-on-mcp-agents-and-the] Stay Tuned for More! 🙋 Join the [https://itot.academy]ITOT.Academy [https://itot.academy] (new cohort in September) → [https://itot.academy]📘 Pre-order the [https://itotbook.com/]IT/OT Handbook [https://itotbook.com/] (+ claim the bonuses!) → [https://itotbook.com/] Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence. 🚀 See you in the next episode! Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider]Apple Podcasts: Spotify Podcasts: This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Kevin Jones from dataPARC on Historians, Industrial Data, Context and a Piece of Black Paper

(Our topic. Our tone. Sponsored by dataPARC [https://www.dataparc.com/] *) Back to our regular schedule, finally. Hannover Messe is in the rear-view mirror, the noise is fading, and we’re easing into one podcast a week again. To get us going, we sat down with Kevin Jones, Director of Partner and Product Strategy at dataPARC [https://www.dataparc.com/], for a conversation that took us all the way back to 1981 — and somehow ended up in a control room in the southeastern US, in the middle of the night, with a piece of black paper taped to a screen. Meet Kevin (and dataPARC) Kevin has spent 26 years at the same company. That’s not a typo. He’s worked on industrial data from every angle imaginable — applications, advanced process control, and for the last two decades, the data architecture and management layer underneath it all. dataPARC itself is a 150-person, globally-distributed team that has stayed laser-focused on one thing: the industrial data stack. Connecting it. Storing it. Moving it between systems. Making it usable for analytics. As Kevin puts it, they’ve thought about expanding into other parts of the business over the years — and consciously decided not to. “That’s been our focus from day one. We just work with industrial plants on their data stack.” In a market where every vendor seems to be drifting into AI, into platforms, into “the data fabric for everything,” that kind of discipline is increasingly rare. And it’s exactly why this conversation went deep on the part of the architecture that gets the least attention but does the most heavy lifting: the historian. Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work. What actually makes a historian It’s tempting, especially if you come from the IT side, to assume any modern database can do the job. Why not just dump time-series data in a data lake and let the hyperscaler figure it out? Kevin’s answer is one we’ll be borrowing for years. “We were once in a meeting where one of the hyperscalers was advertising a use case. They said, ‘we stored 500 values and did all these great things.’ In the industrial world, a big fish tank can have 500 data points.” Industrial historians aren’t dealing with thousands of data points. They’re dealing with thousands to millions of signals, sampled at up to millisecond frequency, retained for years. And — crucially — they’re built so that when an engineer says, give me a thousand tags for the last twelve months because I want to run a machine learning experiment, the answer comes back in seconds, not hours. That’s an architectural decision, not a configuration setting. The other point worth flagging: the cloud-first mindset often forgets the egress side of the equation. Getting data into a cloud system is cheap. Pulling it back out, repeatedly, to compute weighted averages or run rolling analytics, can become eye-wateringly expensive very quickly. From sensor data to semantic layer Time-series data alone isn’t enough, and dataPARC realised this early. Their first commercial historian already had the basics of an asset model — process areas, an organising structure, a way to actually find data without knowing the cryptic tag name. That principle has only grown more important. With AI and large language models entering the picture, the semantic layer is no longer a “nice to have” for analytics teams. It’s the thing that determines whether your AI is repeatable or not. “If we can have a good semantic layer, we can be more repeatable. The use cases are really prevalent and it’s becoming just as important as ever.” Which, of course, brings us to the term that’s been impossible to avoid for the past two years: Unified Namespace. Kevin’s view here is one we share. There’s a broad definition of UNS — having a universal, semantic naming for everything in your enterprise — and a narrower, more branded one that essentially says “MQTT broker on top of your data.” The broad version is foundational. The narrow version, on its own, gives you the most recent value. Useful, but limited. “What happens if there’s a problem with this main flow pump? When did it start? Was it now, or did it start on the night shift? You want to look at your history. So having that universal semantic layer is key — but it should give you the current value and the historical values and be useful when you point a machine learning model at it.” Closing thoughts Forty-something years on from that first homegrown historian at Georgia Pacific, the historian has been declared dead more times than we can count. And every time, it has come back not as a relic but as something more central than before. That’s because, as Kevin put it, all the AI in the world is only as good as the data feeding it. And the data feeding it has to be time-series, has to be contextualised, has to be reliable, and has to be retrievable on demand. Whatever you call that layer of your architecture — historian, time-series database, operational data store — the function isn’t going anywhere. The harder, less glamorous truth is the human one. The piece of black paper taped over a monitor in the middle of the night is a more honest snapshot of where most plants are than any maturity model. Operators have already done the analysis in their heads. They’ve already decided what matters. The job of every IT/OT team is to listen to that, codify it, and build systems that respect it. If your historian is doing its job, the next 2am phone call should be a sign of trust, not failure. About DataParc Founded in 1997, dataPARC [https://www.dataparc.com/] is a comprehensive industrial analytics software suite that helps process manufacturers optimize operations, boost productivity, and drive sustainability. Featuring an enterprise data historian, embedded analytics, and tools for real-time data monitoring, trending, and reporting, dataPARC empowers manufacturers to make smarter, faster decisions that positively impact the bottom line. dataPARC serves customers across the process industries with deployments at thousands of sites globally. Stay Tuned for More! 🙋 Join the [https://itot.academy]ITOT.Academy [https://itot.academy] (new cohort in September) → [https://itot.academy]📘 Pre-order the [https://itotbook.com/]IT/OT Handbook [https://itotbook.com/] (+ claim the bonuses!) → [https://itotbook.com/] Subscribe to our podcast and blog to stay updated on the latest trends in Industrial Data, AI, and IT/OT convergence. 🚀 See you in the next episode! Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: (*) At the IT/OT Insider we do value our independence and transparency. So as we look for ways to pay the bills we were looking for ways to work with sponsors without giving up on those principles. This is where the idea of sponsors comes from. Together with a few selected sponsors we’ll explore some topics that we both find interesting in the same way we write our normal articles. In the coming weeks you’ll find a couple of pieces that have been sponsored. Feel free to contact us if you are interested in a partnership as well. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Aron Semle on MCP, Agents and the Data Foundation You Can’t Skip

In this episode of our podcast, David sits down (again 😀) with Aron Semle, CTO at HighByte, for a follow-up conversation roughly a year after our first podcast together [https://itotinsider.substack.com/p/industrial-dataops-4-with-highbyte]. A lot has changed in a year. When we last spoke, Industrial DataOps was still a concept many people needed convincing about. AI was already making waves, but the link between data foundations and AI readiness wasn’t as obvious to most manufacturing organisations as it is today. So where are we now? And where is the hype outpacing reality? You can listen to the episode, or get the main ideas in this article! Let’s get into it. We Don’t Sell the Problem Anymore The market conversation has shifted in the last year. “We don’t sell the problem anymore,” Aron says. “People understand the problem. We have customers and prospects coming to us saying: we know we need to clean up this data to get AI-ready.” That’s a big shift. And it mirrors what we see at the IT/OT Insider as well. During the peak of the LLM hype, everyone wanted AI but nobody wanted to talk about foundations. Now? People come to us and say: we want AI, and therefore we need to build a foundation. That loop is finally closing. And AI is the catalyst. Sebastian from Frost & Sullivan, who presented at HighByte’s DataOps Days [https://www.highbyte.com/company/events/dataops-days] late last year, made the argument that AI and DataOps are coupled — hand in hand — and showed the market growing accordingly. He’s right. Thanks for reading The IT/OT Insider! Subscribe for free to receive our posts and support our work. Starting from Where You Are So what does that foundation look like in practice? Aron breaks it down using a digital maturity lens: If you’ve got equipment with nothing connected and everything is manual, you’re not ready for AI on the factory floor. You need connectivity first — HMIs, SCADA, historians. If you’re at that level, you’re probably ready for the next step: Industrial DataOps. The way HighByte positions it is straightforward. Industrial DataOps is not going to overlap with your existing systems that run the plant. It’s a layer you put on top — one that connects to all those systems, contextualises the data, and provides access to it from other consumers. AI being one of them. The real work, though, is in harmonisation. “How do you consistently represent equipment that could be the same or completely different across facilities?” Aron asks. That’s the scalability piece. At one factory, the production order comes from an ERP or MES. At another, it’s a CSV file on someone’s desktop. If you’re consuming that data from AI — or from anything else — you shouldn’t need to know the difference. UNS: Valuable, But It Was Always Just the Start We couldn’t have this conversation without touching on Unified Namespace [https://itotinsider.substack.com/p/new-video-the-unified-namespace-explained]. And Aron’s perspective has matured in the same way the market has. Five years ago, UNS was the start of DataOps at the edge: connect to everything, contextualise it, publish it to an MQTT broker. And for someone coming from nothing — like that bakery with independent machines and local HMIs everywhere — being able to subscribe to a broker and see the real-time state of the factory was genuinely transformative. But now? Aron is candid: “Publishing to MQTT is just the transport of real-time data out. That strategy sticks around.” With AI entering the picture, you also need historical data access, governance across multiple sites and contextual dimensions. “It’s not that anything we did is nulled,” Aron clarifies. “It was the first step of the journey towards something bigger.” LLMs and Agents: Let’s Demystify This David pushes for definitions, and Aron delivers them with refreshing clarity. * LLMs are the large neural networks most of us experience through chatbots. You put in a request, a response comes out. The context of the chat goes in each time (or a compressed version), and a new response is generated. * Tool calling is when the LLM detects it can’t answer on its own and reaches out to external tools — this is where MCP comes into play. * Agents are really just the code around the LLM. An agent is an LLM in a loop: instead of a human driving the conversation, the agent code handles tool calls, feeds results back into the context window, and decides when the task is done. How autonomous it is depends entirely on the code you write around that loop. “But make no mistake about it,” Aron says. “We’re just building text documents and feeding them into a large language model. And then stuff comes out that’s sometimes in JSON format that we can code against. It’s not beyond that.” LLMs are probabilistic. They’re hallucinating all the time and they just happen to be right a percentage of the time. In manufacturing, where determinism is fundamental, that’s a real constraint. Now imagine that kind of confident hallucination in a pharmaceutical clean room. Or on a production line with multi-million-euro equipment. “If we’re talking about fully autonomous agents that are going to get control of our machines — very bad idea,” Aron says flatly. MCP: Getting Beat Up, But Here to Stay MCP (Model Context Protocol) has had a steep rise — and Aron acknowledges it’s getting some backlash. That’s normal for anything with a sharp hype curve. His take: “MCP is remote procedure calling for LLMs.“ It provides discovery, remote calling, and a bit of instruction in between. Where it gets criticised is by technologists who look at it and say: this should just be a REST API. The funny thing? “If you go look at the latest version of MCP, it essentially is a REST API,” Aron says. “They got rid of server-sent events and it’s essentially an API now.” The only scenario where MCP loses relevance, in Aron’s view, is if LLMs get good enough to call conventional APIs directly — and those APIs evolve to be more interaction-oriented rather than CRUD-style developer tools. But for HighByte, it doesn’t matter much either way: you’re still building the Industrial DataOps layer, the contextualisation, the custom pipelines. Whether the AI calls that via MCP or REST is an implementation detail. What matters is that the tools are deterministic. “You define the MCP tool, its inputs, its outputs. If you get a call that’s trying to do SQL injection, you detect that and stop it with deterministic logic,” Aron explains. That’s why HighByte’s implementation was never a static, one-size-fits-all tool set. It’s designed so you build your own tools — like APIs — and control exactly what they do. A Quick Teaser: i3X We briefly touched on i3X, a new standard that Aron is heavily involved in alongside Jonathan Weiss and Matthew Paris. We won’t go into detail here — we’ll be recording a dedicated podcast with John Dyck and Jonathan Wise in the coming weeks — but the short version is this: Aron sees i3X as a standardised API layer for the factory. Every vendor already has APIs, but everyone does it their own way. If the industry can standardise how contextual data is accessed across vendors, it removes a massive amount of inefficiency. “If cloud vendors step up and build clients for i3X, that is going to be the ingestion highway in and out of the factory,” Aron says. Version one of the spec is expected before the end of Q2 2026. AI for DataOps: The Slider from Manual to Autonomous Beyond using DataOps to feed AI (which is the foundation story), there’s the flip side: using AI to do DataOps faster. HighByte’s latest release includes what they call a Pipeline AI Agent. Pipelines are their ETL tool for moving and transforming industrial data. The agent lets you prompt an LLM to analyse and edit pipeline configurations. What’s clever is the middle ground it occupies. Aron describes it as an AI slider: On one end, fully manual — you know what you’re doing and you want full control. On the other end, full autonomy — let the AI handle it. The Pipeline AI Agent sits in between: human in the loop. You prompt it, the UI shows you inline what it edited, you review, accept or reject, and iterate. This is where AI genuinely accelerates experts. Not by replacing them, but by handling the tedious parts so they can focus on what requires judgement. Give it a P&ID diagram, an Excel sheet, and access to an OPC server, and let it help you get started with contextualisation. Human in the loop to create deterministic output — absolutely needed — but accelerated by AI. Wrapping Up A year ago, we talked about Industrial DataOps as a discipline. Now it’s a recognised market segment. The conversation has shifted from “why do I need a data foundation?” to “how do I build one so AI can actually deliver?” The takeaway from this conversation? The foundation hasn’t changed. The urgency has. And the hype-to-reality gap on agents and autonomous AI is still very wide. The vendors and practitioners who will win are the ones who are honest about where that gap lies — and focus on the use cases that deliver real value today. HighByte will be at Hannover Messe 2026 (April 20-24) — look for Aron and his colleagues at the AWS, Siemens, and Microsoft booths. Thanks for listening — and thanks to HighByte for sponsoring this one! … and if you haven’t listened to the previous conversation we had with Aron, why not do it now? About HighByte HighByte is an industrial software company founded in 2018 in Portland, Maine USA. The company builds solutions that address the data architecture and integration challenges faced by manufacturers and industrial companies as they digitally transform. HighByte Intelligence Hub, the company’s proven Industrial DataOps software, provides modeled, ready-to-use data to the Cloud using a codeless interface to speed integration time and accelerate analytics. The Intelligence Hub has been deployed in more than a dozen countries by the world’s most innovative companies spanning a wide range of vertical markets, including automotive, energy, food and beverage, life sciences, and mining and metals. 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