XMPro

𝐆𝐚𝐦𝐞 𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐓𝐞𝐚𝐦𝐬 𝐅𝐨𝐫 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐗𝐌𝐏𝐫𝐨 𝐌𝐀𝐆𝐒 ---- XMPro MAGS (Multi-Agent Generative Systems) enables industrial organizations to deploy collaborative AI agent teams that observe, reflect, plan, and act autonomously in real-time. Unlike traditional AI that stops at generating insights, XMPro MAGS delivers autonomous decision intelligence through specialized agents working together as a team: ✅ Agents that observe conditions, reflect on experiences, and take immediate action ✅ Continuous learning through sophisticated memory systems, like human experts ✅ Seamless preservation of institutional knowledge as workforce changes ✅ Integration with existing OT/IT systems through 200+ industrial connectors ✅ Enterprise-grade governance via our APEX platform with deontic principles For more information: XMPro APEX --> https://lnkd.in/gp6-aCHh Github Repository --> https://lnkd.in/gJcFn8iW #IndustrialAI #CollaborativeAI #MultiAgentSystems #DigitalTransformation #OperationalExcellence #AgenticAI #Agents #LLM #GenerativeAI #GenAI #ArtificialIntelligence

AI is reshaping the mining industry, improving efficiency, sustainability, and decision-making. On March 18, XMPro CEO Pieter van Schalkwyk will present at the AI and Applications of Data Working Group Webinar, sharing insights on the Foundations of AI in Mining Project. This session will cover: ✔️ How AI and machine learning are driving real-time optimization and predictive maintenance. ✔️ Practical applications of AI in mining operations. ✔️ The role of data-driven strategies in improving productivity and safety. Pieter will speak alongside: 🔹 Mary A. (Lia) Walker, Freeport-McMoRan – Driving Step Change in Mining 🔹 Rob Johnston, CITIC Pacific Mining – Working Group Update AI is changing the way mining companies operate. Learn how industry leaders are putting it to work. 📌 Register here: https://lnkd.in/ggjARunP

𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐌𝐨𝐝𝐞𝐥 𝐇𝐲𝐩𝐞: 𝐖𝐡𝐞𝐫𝐞 𝐀𝐈'𝐬 𝐓𝐫𝐮𝐞 𝐕𝐚𝐥𝐮𝐞 𝐋𝐢𝐞𝐬 We've all experienced it: a new AI model launches, stocks swing wildly, social media floods with impressive demos... then reality sets in. Remember the market panic during the "DeepSeek moment" or the breathless coverage of Manus? Just the latest examples of our fixation on individual models. Working with XMPro has revealed a crucial truth: AI models themselves are becoming commodities. The real value lies in business operation intelligence systems that embed these models within end-to-end business processes. Why business operation intelligence systems matter more than any flashy new model: ► A single breakthrough model is impressive; a complete intelligence system that orchestrates traditional analytics, machine learning, and generative AI is transformative ► When a standalone model fails, work stops completely; comprehensive intelligence platforms maintain business continuity even when components fail ► Models age quickly with each release; well-designed business intelligence systems evolve continuously without disruption ► Generic models struggle with specific industry challenges; complete operation intelligence platforms adapt to your unique operational requirements ► Models generate ideas; intelligent operation systems turn those ideas into protected IP and measurable business outcomes The quiet revolution isn't happening in research labs releasing new models—it's happening on factory floors and in operations centers where complete business intelligence platforms are transforming disconnected AI capabilities into sustainable operational excellence. What about you? Is your organization still jumping from model to model, or are you building a comprehensive intelligence system that can evolve beyond today's AI hype cycle? Share your experience or challenges below! #ArtificialIntelligence #AIStrategy #BusinessTransformation #IndustrialAI

𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐌𝐨𝐝𝐞𝐥 𝐇𝐲𝐩𝐞: 𝐖𝐡𝐞𝐫𝐞 𝐀𝐈'𝐬 𝐓𝐫𝐮𝐞 𝐕𝐚𝐥𝐮𝐞 𝐋𝐢𝐞𝐬 We've all experienced it: a new AI model launches, stocks swing wildly, social media floods with impressive demos... then reality sets in. Remember the market panic during the "DeepSeek moment" or the breathless coverage of Manus? Just the latest examples of our fixation on individual models. Working with XMPro has revealed a crucial truth: AI models themselves are becoming commodities. The real value lies in business operation intelligence systems that embed these models within end-to-end business processes. Why business operation intelligence systems matter more than any flashy new model: ► A single breakthrough model is impressive; a complete intelligence system that orchestrates traditional analytics, machine learning, and generative AI is transformative ► When a standalone model fails, work stops completely; comprehensive intelligence platforms maintain business continuity even when components fail ► Models age quickly with each release; well-designed business intelligence systems evolve continuously without disruption ► Generic models struggle with specific industry challenges; complete operation intelligence platforms adapt to your unique operational requirements ► Models generate ideas; intelligent operation systems turn those ideas into protected IP and measurable business outcomes The quiet revolution isn't happening in research labs releasing new models—it's happening on factory floors and in operations centers where complete business intelligence platforms are transforming disconnected AI capabilities into sustainable operational excellence. What about you? Is your organization still jumping from model to model, or are you building a comprehensive intelligence system that can evolve beyond today's AI hype cycle? Share your experience or challenges below! #ArtificialIntelligence #AIStrategy #BusinessTransformation #IndustrialAI

Nicolas Waern - Digital Twin Specialist posted an interesting Vitruvian principles comment on my article on "Beyond Code: How Parametric Agents Transform Industrial AI Implementation." (https://lnkd.in/grcknR-V). Here is a short reply 😏 Nicolas' comment made me think about how our parametric agents follow these ancient but powerful design truths. Vitruvius showed us that success comes from working with reality rather than against it. His principles - durability, utility, and elegance - map perfectly to how MAGS operates in complex industrial environments. Let me share how these principles appear in our system: 📌 Firmitas: Durability means our agents operate within clear physical and operational boundaries. Parameters reflect actual equipment limitations and safety constraints rather than arbitrary rules in code. 📌 Utilitas: Utility lets subject matter experts shape agent behavior using concepts they already understand. Operators work with familiar equipment data and visual displays instead of learning to code. 📌 Venustas: Elegance comes from separating what agents can do from how they're controlled. This creates simpler solutions that operators can adjust without changing underlying systems. #MAGS #AgenticAI #MultiAgents

𝐈𝐓 𝐯𝐬. 𝐎𝐓 – 𝐀 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞, 𝐀 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐢𝐧𝐠 𝐃𝐢𝐯𝐢𝐝𝐞 The friction between IT (Information Technology) and OT (Operational Technology) is one of priorities and perspectives: 🔹 IT is focused on security, governance, and enterprise-wide standardization. 🔹 OT prioritizes real-time control, uptime, and operational agility. Yet, IT/OT convergence isn’t optional—it’s critical. Organizations must bridge these two worlds to unlock predictive insights, automation, and intelligent decision-making. But this convergence introduces major challenges: 🚧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐯𝐬. 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 – IT enforces cybersecurity, while OT needs always-on systems. ⚙️ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐯𝐬. 𝐋𝐞𝐠𝐚𝐜𝐲 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 – IT prefers cloud and scalable solutions; OT relies on long-lifecycle industrial systems. 📊 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐯𝐬. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 – IT structures data for analytics; OT must make split-second decisions at the edge. 🔧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐯𝐬. 𝐀𝐠𝐢𝐥𝐢𝐭𝐲 – IT wants controlled, standardized solutions, while OT needs flexibility to adapt quickly. 🤝 𝐔𝐧𝐜𝐥𝐞𝐚𝐫 𝐎𝐰𝐧𝐞𝐫𝐬𝐡𝐢𝐩 & 𝐅𝐮𝐧𝐝𝐢𝐧𝐠 – Many organizations struggle with defining who leads IT/OT convergence and how it’s funded. Four Pillars of IT/OT Convergence 🔹 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 – Connecting industrial assets (PLCs, sensors, controllers) to IT networks 🔹 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 – Integrating enterprise IT (ERP, CRM) with OT systems (MES, SCADA) 🔹 𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 – Unifying IT & OT data 🔹 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 – Synchronizing business processes with shop-floor workflows. 𝐗𝐌𝐏𝐫𝐨: 𝐓𝐡𝐞 𝐁𝐫𝐢𝐝𝐠𝐞 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐎𝐓 & 𝐈𝐓: This is where XMPro makes the difference. Our Intelligent Business Operations Suite (iBOS) is built to satisfy both IT and OT teams by: ✅ Seamless IT/OT Connectivity – XMPro integrates industrial assets (SCADA, sensors, machinery) with enterprise IT systems (AI, analytics, cloud)—enabling a unified operational strategy. ✅ Real-Time Data Utilization – XMPro provides enhanced visibility into operations, allowing teams to monitor performance, detect anomalies, and respond instantly to critical events. ✅ Predictive & Prescriptive Insights – Going beyond traditional data analysis, XMPro enables AI-driven recommendations to prevent failures, optimize workflows, and drive efficiency. ✅ Empowering SMEs with No-Code Tools – OT teams can rapidly compose and deploy solutions without needing IT for every change. XMPro’s no-code platform allows SMEs to configure workflows and automate processes—without programming. ✅ Scalability & Long-Term Adaptability – XMPro evolves with your business as it grows in its digital transformation journey. With XMPro, IT and OT don’t have to compete—they can collaborate seamlessly to create a secure, agile, and data-driven future.

𝐁𝐨𝐮𝐧𝐝𝐞𝐝 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲: 𝐀 𝐏𝐫𝐚𝐠𝐦𝐚𝐭𝐢𝐜 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 Autonomous AI agents shouldn't be an all-or-nothing debate. Pieter van Schalkwyk breaks down the real challenge—balancing autonomy with governance—using XMPro’s Multi-Agent Generative Systems (MAGS). Instead of fearing unbounded autonomy, we need structured independence: ✅ Computational Policies – Enforcing fundamental constraints ✅ Deontic Principles – Encoding ethical guardrails ✅ Expert System Rules – Leveraging domain knowledge This approach ensures AI agents are powerful, safe, and scalable—without losing human oversight. A must-read for anyone working with industrial AI. 🚀

New on YouTube! "Multi-Agent Generative Systems in Industrial Applications" Presented by Pieter van Schalkwyk, XMPro This presentation introduces Multi-Agent Generative Systems (MAGS), an advanced AI-driven platform that integrates multiple software agents with large language models to optimize industrial processes. Unlike traditional multi-agent systems, MAGS agents can reason, collaborate, and adapt to complex environments. In this clip, Pieter provides a definition of MAGS and breaks it down into three primary categories. Pieter explores how MAGS agents observe their environment, reflect on past experiences, plan actions, and execute tasks. He highlights the crucial role of digital twins in providing a real-time proxy for physical assets, enabling continuous monitoring and simulation, including: 1. Advanced reasoning capabilities 2. Collaborative problem-solving among agents 3. Adaptive decision-making in dynamic environments 4. Integration with real-time data and business applications Pieter concludes with a live demonstration of MAGS managing an industrial business process using digital twins, showcasing its potential to transform operations through intelligent automation and continuous optimization. Watch the entire video on our YouTube channel and subscribe: https://bit.ly/40OXL75 #MultiAgentGenerativeSystems #MAGS #AI #ArtificialIntelligence #IndustrialSector #ManufacturingSector #IntelligentAutomation

New on YouTube! "Multi-Agent Generative Systems in Industrial Applications" Presented by Pieter van Schalkwyk, XMPro This presentation introduces Multi-Agent Generative Systems (MAGS), an advanced AI-driven platform that integrates multiple software agents with large language models to optimize industrial processes. Unlike traditional multi-agent systems, MAGS agents can reason, collaborate, and adapt to complex environments. In this clip, Pieter provides a definition of MAGS and breaks it down into three primary categories. Pieter explores how MAGS agents observe their environment, reflect on past experiences, plan actions, and execute tasks. He highlights the crucial role of digital twins in providing a real-time proxy for physical assets, enabling continuous monitoring and simulation, including: 1. Advanced reasoning capabilities 2. Collaborative problem-solving among agents 3. Adaptive decision-making in dynamic environments 4. Integration with real-time data and business applications Pieter concludes with a live demonstration of MAGS managing an industrial business process using digital twins, showcasing its potential to transform operations through intelligent automation and continuous optimization. Watch the entire video on our YouTube channel and subscribe: https://bit.ly/40OXL75 #MultiAgentGenerativeSystems #MAGS #AI #ArtificialIntelligence #IndustrialSector #ManufacturingSector #IntelligentAutomation

🤔 Why can't we just document everything our experienced operators know? Industrial companies are losing decades of operational expertise as experienced workers retire. Many think the solution lies in better documentation and content-based automation. But here's the reality: You can't capture human judgment in a procedure manual or workflow. In my latest article, I explore why cognitive agents represent the missing piece in industrial automation. Learn: • Why content-based automation isn't enough • How cognitive agents actually make operational decisions • Why preserving expert knowledge requires more than documentation • How XMPro helps companies maintain operational excellence even as their workforce changes #IndustrialAutomation #CognitiveAgents #OperationalExcellence #Manufacturing #ProcessIndustry #MAGS #DigitalTwins Thoughts? I'd love to hear your experiences with preserving operational expertise in your organization.