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XMPro Platform
XMPro Platform
  • What is XMPro?
  • Getting Started
    • Browser Requirements
    • Free Trial
    • End-To-End Use Case
  • Resources
    • What's New in 4.4
      • What's New in 4.3
      • What's New in 4.2
      • What's New in 4.1.13
      • What's New in 4.1
      • What's New in 4.0
    • Blueprints, Accelerators & Patterns
    • Integrations
    • Sizing Guideline
    • Platform Security
    • Icon Library
    • FAQs
      • Implementation FAQs
      • Configuration FAQs
      • Agent FAQs
      • General FAQs
      • External Content
        • Blogs
          • 2024
            • How to Build Multi-Agent Systems for Industry
            • Why Solving the Problem Doesn’t Solve the Problem: The Importance of Scalable Intelligent Operations
            • Content, Decision, and Hybrid: The Three Pillars of Multi-Agent Systems in Industry
            • Revolutionizing Manufacturing with AI and Generative AI: XMPro’s Intelligent Business Operations Sui
            • The Evolution of Skills: Lessons from Agriculture in the GenAI and MAGS Era
            • Part 1: From Railroads to AI: The Evolution of Game-Changing Utilities
            • Part2: The Future of Work: Harnessing Generative Agents in Manufacturing
            • Bridging Automation and Intelligence: XMPro’s Approach to Industrial Agent Management
            • XMPro APEX: Pioneering AgentOps for Industrial Multi Agent Generative Systems
            • Part 5 – Rules of Engagement: Establishing Governance for Multi-Agent Generative Systems
            • How to Achieve Scalable Predictive Maintenance for Industrial Operations
            • Understanding the Difference Between XMPro AI Assistant and AI Advisor
            • Part 3 – AI at the Core: LLMs and Data Pipelines for Industrial Multi-Agent Generative Systems
            • MAGS: The Killer App for Generative AI in Industrial Applications
            • The Importance of Pump Predictive Maintenance for Operational Efficiency
            • Progressing Through The Decision Intelligence Continuum With XMPro
            • The Value-First Approach to Industrial AI: Why MAGS Implementation Must Start with Business Outcomes
            • New Guide – The Ultimate Guide to Multi-Agent Generative Systems
            • The Ultimate Guide To Predictive Analytics
            • Part 4 – Pioneering Progress | Real-World Applications of Multi-Agent Generative Systems
            • Scaling Multi-Agent Systems with Data Pipelines: Solving Real-World Industrial Challenges
          • 2023
            • How to master Predictive Analytics using Composable Digital Twins
            • Accelerate Your AI Workflow: The 3 Key Business Advantages of XMPro Notebook
            • The Roadmap to Intelligent Digital Twins
            • What is edge computing, and how can digital twins utilize this technology?
            • THE TOP 5 USE CASES FOR COMPOSABLE DIGITAL TWINS IN RENEWABLES + HOW TO SUPERCHARGE RESULTS WITH AI
            • The Technology Behind Predictive Maintenance (PdM) : Hardware & Software
            • The Benefits of Using Digital Twins in Smart Manufacturing
            • XMPro I3C Intelligent Digital Twins Strategy Framework
            • The TOP 5 use cases for composable digital twins in mining – and how to use AI to supercharge result
            • The TOP 5 use cases for Composable Digital Twins in the Oil & Gas industry
            • Why Decision Intelligence with Digital Twins is “kinda like” DCS for Automation and Control
            • XMPro becomes an NVIDIA Cloud-Validated partner
            • From Reactive to Predictive : Introduction to Predictive Maintenance
            • Microsoft Azure Digital Twins : Everything You Need To Know
            • Unlocking Efficiency: The Right Time & Strategy to Launch Your Digital Twin for Enhanced Asset Manag
            • Revolutionize Your Supply Chain: How Digital Twins Can Boost Efficiency and Cut Costs
          • 2022
            • Create a Common Operating Picture of Your Operations with XMPro
            • 7 Trends for Industrial Digital Twins in 2022
            • How to Build a Digital Twin + 60 Use Cases By Industry
            • What are composable digital twins in the metaverse?
          • 2021
            • The Value of a Composable Digital Twin
          • 2020
            • Lean Digital Twin: Part 2
            • Digital Twin: Your Most Productive Remote Worker
            • From the Control Room to the Bedroom
            • Lean Digital Twin: Part 3
          • 2019
            • My Digital Twin: Digital Twin Applications for Real-time Operations (Like Me)
          • 2018
            • XMPro IoT Operational Capability Survey Results 2018
            • What is a Digital Business Platform and Why Should I Care?
            • [Robotic] Process Automation for IoT
            • 3 Patterns of Industrial IoT Use Cases
            • The CXO’s Guide to Digital Transformation – May The Five Forces Be With You
            • Is Security More Important Than Trustworthiness for Industrial IoT?
            • XMPro at bpmNEXT 2018: Watch The Presentation
          • 2017
            • The Top 5 Reasons to Invest in an IIoT Development Platform
            • IoT Business Solutions Start with Big Data & Create Business Outcomes
            • How AI Bots Bring Digital Twins to Life
          • 2016
            • How To Get Started With Industrial IoT
            • How To Overcome The Top 5 Challenges To Industrial IoT Adoption
            • What is an IoT Platform vs. an IoT Business Application Suite?
            • Industrial IoT: How To Get Started with Predictive Maintenance
            • 3 Ways The Internet of Things is Transforming Field Service
            • 7 Types of Industrial IoT Data Sources (And How To Use Them)
          • 2015
            • How Important Are Processes To The Internet Of Things?
            • Understanding the Value of Real Time KPI Management as Your Next Strategic Project
            • 6 Myths About Machine Learning
            • 10 Predictive Analytics Use Cases By Industry
            • What is a “Business Moment” in your business?
            • Does Operational Intelligence Make Business Intelligence Obsolete?
            • How To Reduce Operational Costs by 36% with Predictive Analytics
            • From Many, One – The Nature of Complex Event Processing
            • Herding Cats: What Enterprise Architects need to know about Business Process Management
          • 2014
            • Making Business Operations More Intelligent
          • 2013
            • Best Next Action Is The Next Big Thing For Intelligent Operations
            • The learns from two ‘Best in class’ organisations acquiring BPM technology
          • 2012
            • Why Intelligent Business Operations is Mobile, Social and Smart
            • Why Do You Want Intelligent Business Operations?
            • How big of a problem are ‘dark processes’?
            • Operational Risk: When You Stick Your Head In The Sand
            • The Difference Between Event-based And Workflow-based Processes
          • 2011
            • Is mobile BPM now essential to the business?
            • Stretch Socially Dynamic Processes To Fit Your Business
            • Social Listening – Get Control Of The Conversation
            • Operations Management – The Keys To KPIs
            • Benefits of BPM v 1.0
            • How to Prioritise Processes
          • 2010
            • The Business Drivers
            • Preserving Capability and Agility
            • Mobile BPM
        • Use Cases
          • Aging Pipe Predictive Maintenance in Water Utilities
          • Air Quality Monitoring For Agriculture
          • Alarm Management and Triage
          • Asset Condition Monitoring for Surface Processing Plants in the Mining Industry
          • Bogie Health Monitoring in the Rail Industry
          • Boiler Feed Water Pumps
          • CHPP Throughput Loss Monitoring
          • Casting Guidance
          • Conveyor Belt System Monitoring and Optimization in Automotive Manufacturing
          • Cooling Tower Fin Fan Monitoring
          • Cyclone/Slurry Pump Monitoring
          • Demand Planning to Reduce Stockholding in Stores
          • Demin Water Monitoring for Boiler Tube Corrosion
          • EV Battery Assembly Process Optimization for the Car Manufacturing Industry
          • Flood Prediction & Response in Water Utilities
          • Golden Batch For Culture Addition In The Dairy processing Industry.
          • Golden Batch Monitoring
          • Improve First Pass Yield (FPY)
          • Induced Draft (ID) Fan Monitoring
          • Long Conveyor Monitoring
          • Monitor Process Health to Reduce Cash-to-Cash Cycle
          • Monitor Storm Water Reservoirs For Flood Prevention
          • Monitor and Reduce Energy Consumption
          • Oil Well Maintenance Planning
          • Oil Well RTP Monitoring
          • Pipe Scaling Prediction for Roller Cooling
          • Precision Irrigation in Agriculture
          • Predict Heat Exchanger Fouling
          • Predictive Maintenance & Asset Health Monitoring For Haul Trucks In The Mining Industry
          • Predictive Maintenance For Mobile Assets Within The Mining Industry
          • Predictive Maintenance for Robotic Arms in the Automotive Industry
          • Predictive Maintenance for Wind Turbines
          • Pump Health Monitoring in Water Utilities
          • Pumping Station OEE
          • Real-time Balanced Business Scorecard (BBS)
          • Real-time Safety Monitoring
          • Short Term Inventory Planning
          • Strategic Performance & Safety Oversight for Global Mining Operations
          • Wheel and Track Wear Monitoring In The Rail Industry
          • Wind Turbine Performance Optimization
        • Youtube
          • 2024
            • Discover Gen AI Powered Operations With XMPro iBOS
            • Generative AI and Digital Twins in 2024 - XMPro Webinar
            • Go From Reactive To Predictive Operations In Water Utilities With XMPro iDTS
            • How to add Timestamps to Elements in XMPro App Designer
            • How to Build an AI Advisor for Industrial Operations Using XMPro
            • How XMPro Stream Hosts and Collections Enable Scalable, Real-Time Data Processing
            • Mind Blowing AI Agentic Operations For Industry With XMPro MAGS
            • The Ultimate Beginner's Guide To Predictive Analytics Podcast
            • XMPro's Flexible Deployment Options: Flexible Cloud & On-Premise Solutions For Industry
            • XMPro iBOS: The Only AI-Powered Suite for Scalable Intelligent Operations
          • 2023
            • 2023 XMPro Product Roadmap - Webinar
            • An Introduction To Intelligent Digital Twins - Webinar
            • Energy and Utilities Asset Optimisation through Digital Twin technology
            • Explore Model Governance using our MLflow Agent
            • Exploring XMPro Notebook and MLflow for Data Science and Model Governance
            • How Changing Properties For One Block Can Be Applied To All Blocks Within Same Style Group
            • How do I Use A Button To Update a Data Source In XMPro App Designer
            • How Does XMPro Compare To ESBs (Enterprise Service Buses)-
            • How to Configure and Integrity Check in Data Streams
            • How To Create A Widget Within XMPro App Designer
            • How to Create Intelligent Digital Twins Using XMPro AI
            • How to export grid data to Excel In XMPro App Designer
            • How to Revolutionize Your Supply Chain with Digital Twins
            • How To Rotate Text In App Designer
            • How To Update a Data Source Using A Button
            • How To Use & Clone XMPro Demos For Your Own Use
            • How To Use And Build 3rd Party Apps To Extend The Capabilities Of The XMPro App Designer.
            • How to use Avatars and why they are important
            • How to view stream host logs In XMPro Data Stream Designer
            • Logging Provider Support With XMPro
            • Mastering Health Check Endpoints: A Guide to Ensuring Service Uptime and Performance with XMPro
            • Mastering Root Cause Analysis with XMPro: Capture, Value, Impact
            • Microsoft Azure Digital Twins Everything You Need To Know
            • Model Based Predictive Maintenance (PdM) With XMPro
            • Monthly Webinar - Accelerate your digital twin use cases - XMPro Blueprints, Accelerators & Patterns
            • Optimizing Time Series Chart (TSC) Performance
            • Predictive Maintenance & Condition Monitoring - A Hot Seat Q&A Session
            • Predictive Maintenance with XMPro iDTS
            • Smart Facilities Management with Intelligent Digital Twins
            • The Benefits of using Digital Twins in Smart Manufacturing
            • The Four Industrial Revolutions Explained In Under 4 Minutes! #industry4 #smartmanufacturing
            • The Roadmap To Intelligent Digital Twins
            • The Technology Behind Predictive Maintenance (PdM) - The Hardware & Software that makes PdM Tick...
            • THE TOP USE CASES FOR COMPOSABLE DIGITAL TWINS IN RENEWABLES
            • Tips on how to use cache in agent configuration and get live updates
            • Webinar - XMPro 4.3 Release Showcase
            • What is a Digital Twin- Why Composable Digital Twins is the Future.
            • What Is Predictive Maintenance- (PdM)
            • What To Do When a Data Source Is Not Showing in Pass Page Parameter
            • XMPro - The World's Only AI - Powered Intelligent Digital Twin Suite
            • XMPro - The World's Only No Code Digital Twin Composition Platform
            • XMPro AI : How It Works
            • XMPro AI End To End Use Case
            • XMPro Auto Scale - Understanding Distributed Caching for Cloud-Native Applications
            • XMPro Promo Video - Dell Validated Design For Manufacturing Edge
          • 2022
            • Aggregate Transformation Agent Example - XMPRO Data Stream Designer
            • App Layout Best Practices for Desktop & Mobile - XMPro Lunch & Learn
            • Broadcast Transformation Agent Example - XMPRO Data Stream Designer
            • Calculated Field Transformation Agent Example - XMPRO Data Stream Designer
            • CRC16 Function Agent Example - XMPRO Data Stream Designer
            • Create a Common Operating Picture of Your Operations with XMPro
            • CSV Context Provider Agent Example - XMPro Data Stream Designer
            • CSV Simulator Agent Example - XMPRO Data Stream Designer
            • CSV Writer Agent Example - XMPRO Data Stream Designer
            • Data Conversion Transformation Agent Example - XMPro Data Stream Designer
            • Digital Twin Strategy To Execution Pyramid - XMPro Webinar
            • Event Printer Action Agent Example - XMPRO Data Stream Designer
            • File Listener Agent Example - XMPRO Data Stream Designer
            • Filter Transformation Agent Example - XMPRO Data Stream Designer
            • Group & Merge Transformation Agent Example - XMPRO Data Stream Designer
            • How To Bind Data To A Chart and Get It Working As Expected - XMPro Lunch & Learn
            • How To Send Data To My App (Including Caching Introduction) - XMPro Lunch & Learn
            • Join Transformation Agent Example - XMPRO Data Stream Designer
            • Min/Max Function Agent Example - XMPRO Data Stream Designer
            • PART 1- How To Manage Complex Operations in Real-time Using Composable Digital Twins
            • PART 3 - How To Manage Complex Operations in Real-time Using Composable Digital Twins
            • PART2 - How To Manage Complex Operations in Real-time Using Composable Digital Twins
            • Pass Through Agent Example - XMPRO Data Stream Designer
            • Pivot Table Transformation Agent Example - Count - XMPRO Data Stream Designer
            • Pivot Table Transformation Agent Example - Sum - XMPRO Data Stream Designer
            • Real-Time Is Real - How To Use Event Intelligence Tools to Manage Complex Operations in Real-time.
            • Row Count Agent Example - XMPRO Data Stream Designer
            • Sort Transformation Agent Example - XMPRO Data Stream Designer
            • Transpose Transformation Agent Example - Columns - XMPRO Data Stream Designer
            • Transpose Transformation Agent Example - Rows - XMPRO Data Stream Designer
            • Trim Name Transformation Agent Example - XMPRO Data Stream Designer
            • Twilio Action Agent Example - XMPRO Data Stream Designer
            • Union Transformation Agent Example - XMPRO Data Stream Designer
            • Variables & Expressions in App Designer - XMPro Lunch & Learn
            • Window Transformation Agent Example - XMPRO Data Stream Designer
            • XML File Reader Action Agent Example - XMPRO Data Stream Designer
          • 2021
            • The Value of a Composable Digital Twin - XMPro Webinar
          • 2020
            • 1. Understanding The Problem - UX Design - XMPRO
            • 1.1 Welcome - XMPRO UI Design Basics
            • 1.2 Introduction To UI Design - XMPRO UI Design Basics
            • 2. Creating User Stories - UX Design - XMPRO
            • 2.1 Responsive Design - XMPRO UI Design Basics
            • 2.2 Grids - XMPRO UI Design Basics
            • 2.3 Visual Hierarchy - XMPRO UI Design Basics
            • 2.4 Wireframes - XMPRO UI Design Basics
            • 3. Creating User Flow Diagrams - UX Design - XMPRO
            • 3.1 Color Palette - XMPRO UI Design Basics
            • 3.2 Typography - XMPRO UI Design Basics
            • 3.3 White Space - XMPRO UI Design Basics
            • 3.4 UI Elements - XMPRO UI Design Basics
            • 4. Plan Your App with Wireframes - UX Design - XMPRO
            • 4.1 Chart Types - XMPRO UI Design Basics
            • 4.2 Chart Styling - XMPRO UI Design Basics
            • 5. Designing for Dynamic Data - UX Design - XMPRO
            • Agents and Their Types - XMPRO Data Stream Designer
            • Data Wrangling: Row Transpose - XMPRO Data Stream Designer
            • Digital Twin: Your Most Productive Remote Worker - XMPRO Webinar
            • End-To-End Real-Time Condition Monitoring Demo - XMPRO Application Development Platform
            • Error Endpoints - XMPRO Data Stream Designer
            • Export and Import Recommendations - XMPRO App Designer
            • How To Add Buttons To Agents - XMPRO Data Stream Designer
            • How To Add EditLists to Agents - XMPRO Data Stream Designer
            • How To Change UI Language - XMPRO Subscription Manager
            • How To Configure a Stream Object - XMPRO Data Stream Designer
            • How To Configure The Aggregate Transformation - XMPRO Data Stream Designer
            • How To Configure The Anomaly Detection Agent - XMPRO Data Stream Designer
            • How To Configure The Azure SQL Action Agent - XMPRO Data Stream Designer
            • How To Configure The Azure SQL Context Provider - XMPRO Data Stream Designer
            • How To Configure The Azure SQL Listener - XMPRO Data Stream Designer
            • How To Configure The Calculated Field Transformation - XMPRO Data Stream Designer
            • How To Configure The CSV Context Provider - XMPRO Data Stream Designer
            • How To Configure The CSV Listener - XMPRO Data Stream Designer
            • How To Configure The Data Conversion Transformation - XMPRO Data Stream Designer
            • How To Configure The Edge Analysis Transformation - XMPRO Data Stream Designer
            • How To Configure The Email Action Agent - XMPRO Data Stream Designer
            • How To Configure The Email Listener - XMPRO Data Stream Designer
            • How To Configure The Event Printer Action Agent - XMPRO Data Stream Designer
            • How To Configure The Event Simulator Listener - XMPRO Data Stream Designer
            • How To Configure The FFT Function - XMPRO Data Stream Designer
            • How To Configure The File Listener - XMPRO Data Stream Designer
            • How To Configure The Filter Transformation - XMPRO Data Stream Designer
            • How To Configure The IBM Maximo Action Agent - XMPRO Data Stream Designer
            • How To Configure The IBM Maximo Context Provider - XMPRO Data Stream Designer
            • How To Configure The IBM Maximo Listener - XMPRO Data Stream Designer
            • How To Configure The Join Transformation - XMPRO Data Stream Designer
            • How To Configure The JSON File Reader Context Provider - XMPRO Data Stream Designer
            • How To Configure The MQTT Action Agent - XMPRO Data Stream Designer
            • How To Configure The MQTT Advanced Action Agent - XMPRO Data Stream Designer
            • How To Configure The MQTT Advanced Listener - XMPRO Data Stream Designer
            • How To Configure The MQTT Listener - XMPRO Data Stream Designer
            • How To Configure The Normalize Fields Function - XMPRO Data Stream Designer
            • How To Configure The OSIsoft PI Context Provider - XMPRO Data Stream Designer
            • How To Configure The OSIsoft PI Listener - XMPRO Data Stream Designer
            • How To Configure The Pass Through Transformation - XMPRO Data Stream Designer
            • How To Configure The PMML Agent - XMPRO Data Stream Designer
            • How To Configure The REST API Context Provider - XMPRO Data Stream Designer
            • How To Configure The RScript Agent - XMPRO Data Stream Designer
            • How To Configure The Run Recommendation Agent - XMPRO Data Stream Designer
            • How To Configure The Signal Filter - XMPRO Data Stream Designer
            • How To Configure The SQL Server Action Agent - XMPRO Data Stream Designer
            • How To Configure The SQL Server Context Provider - XMPRO Data Stream Designer
            • How To Configure The SQL Server Listener - XMPRO Data Stream Designer
            • How To Configure The SQL Server Writer Action Agent - XMPRO Data Stream Designer
            • How To Configure The Twilio Action Agent - XMPRO Data Stream Designer
            • How To Configure The Union Transformation - XMPRO Data Stream Designer
            • How To Configure The Unzip Function - XMPRO Data Stream Designer
            • How To Configure The Window Transformation - XMPRO Data Stream Designer
            • How To Create an App - XMPRO App Designer
            • How To Create and Manage Templates - XMPRO App Designer
            • How To Create and Publish a Use Case - XMPRO Data Stream Designer
            • How To Create and Use a Widget - XMPRO App Designer
            • How To Create App Data Connections - XMPRO App Designer
            • How To Create App Pages and Navigation - XMPRO App Designer
            • How To Create Recommendation Rules - XMPRO App Designer
            • How To Create Recurrent Data Streams - XMPRO Data Stream Designer
            • How To Do Integrity Checks - XMPRO Data Stream Designer
            • How To Edit Page Properties - XMPRO App Designer
            • How To Enable Audit Trails - XMPRO App Designer
            • How to Export, Import, and Clone a Data Stream - XMPRO Data Stream Designer
            • How To Export, Import and Clone an App - XMPRO App Designer
            • How to Export and Import an App - XMPRO App Designer
            • How To Find Help for an Agent - XMPRO Data Stream Designer
            • How To Install The XMPRO App Designer
            • How To Maintain and Capture Notes - XMPRO App Designer
            • How To Manage Agents - XMPRO Data Stream Designer
            • How To Manage and Use Server Variables - XMPRO Data Stream Designer
            • How To Manage Buffer Size - XMPRO Data Stream Designer
            • How to Manage Categories - XMPRO App Designer
            • How To Manage Categories - XMPRO Data Stream Designer
            • How To Pass Parameters Between Pages - XMPRO App Designer
            • How To Publish and Share an Application - XMPRO App Designer
            • How To Set Up and Use Charts in Live View - XMPRO Data Stream Designer
            • How To Set Up and Use Gauges in Live View - XMPRO Data Stream Designer
            • How To Share a Data Stream - XMPRO Data Stream Designer
            • How To Share a Use Case - XMPRO Data Stream Designer
            • How To Share an App For Design Collaboration - XMPRO App Designer
            • How To Troubleshoot a Use Case - XMPRO Data Stream Designer
            • How To Upgrade a Stream Object Version - XMPRO Data Stream Designer
            • How To Use App Files - XMPRO App Designer
            • How To Use Application Versions - XMPRO App Designer
            • How To Use Bar Gauge - XMPRO App Designer
            • How To Use Calendar - XMPRO App Designer
            • How To Use Chart Pan, Zoom and Aggregation - XMPRO App Designer
            • How To Use Chart Panes and Axes - XMPRO App Designer
            • How To Use Chart Print and Export- XMPRO App Designer
            • How To Use Charts - XMPRO App Designer Toolbox
            • How To Use Charts: Series - XMPRO App Designer
            • How To Use Collections - XMPRO Data Stream Designer
            • How To Use Content Card - XMPRO App Designer
            • How To Use D3 - XMPRO App Designer
            • How To Use Data Sources - XMPRO App Designer
            • How To Use Embedded Pages - XMPRO App Designer Toolbox
            • How To Use Fieldset and Field - XMPRO App Designer Toolbox
            • How To Use Flex Layout
            • How To Use Form Validation - XMPRO App Designer Toolbox
            • How To Use Input Mappings - XMPRO Data Stream Designer
            • How To Use Linear Gauges - XMPRO App Designer
            • How To Use Live View - XMPRO Data Stream Designer
            • How To Use Lookup - XMPRO App Designer
            • How To Use Maps - XMPRO App Designer
            • How To Use Page Layers - XMPRO App Designer
            • How To Use Pivot Grid - XMPRO App Designer
            • How To Use Polar Charts - XMPRO App Designer
            • How To Use Power BI - XMPRO App Designer
            • How To Use Radio Buttons - XMPRO App Designer Toolbox
            • How To Use Recommendations - XMPRO App Designer Toolbox
            • How To Use Select Box - XMPRO App Designer
            • How To Use Stacked Layouts - XMPRO App Designer Toolbox
            • How To Use Stream Host Local Variables - XMPRO Data Stream Designer
            • How To Use Tabs - XMPRO App Designer Toolbox
            • How To Use Tags - XMPRO App Designer Toolbox
            • How To Use Templated List - XMPRO App Designer
            • How To Use Templates - XMPRO App Designer
            • How To Use Text - XMPRO App Designer Toolbox
            • How To Use Text Area - XMPRO App Designer Toolbox
            • How To Use The Accordion - XMPRO App Designer Toolbox
            • How To Use The Block Styling Manager - XMPRO App Designer
            • How To Use The Box and Data Repeater Box - XMPRO App Designer Toolbox
            • How To Use The Button - XMPRO App Designer Toolbox
            • How To Use The Circular Gauge - XMPRO App Designer Toolbox
            • How To Use The Data Grid - XMPRO App Designer Toolbox
            • How To Use The HTML Editor - XMPRO App Designer Toolbox
            • How To Use The Hyperlink and Box Hyperlink - XMPro App Designer Toolbox
            • How To Use The Image - XMPRO App Designer Toolbox
            • How To Use The Indicator - XMPRO App Designer Toolbox
            • How To Use The Layout Grid - XMPRO App Designer Toolbox
            • How To Use The Number Selector - XMPRO App Designer Toolbox
            • How To Use The Pie Chart - XMPRO App Designer Toolbox
            • How To Use The Range Slider - XMPRO App Designer Toolbox
            • How To Use The Recommendation Chart - XMPRO App Designer Toolbox
            • How To Use The Scroll Box - XMPRO App Designer Toolbox
            • How To Use The Select Box - XMPRO App Designer Toolbox
            • How To Use The Sparkline - XMPRO App Designer Toolbox
            • How To Use The Textbox - XMPRO App Designer Toolbox
            • How To Use Tree Grid - XMPRO App Designer
            • How To Use Tree List - XMPRO App Designer
            • How To Use Unity - XMPRO App Designer Toolbox
            • How To Use Variables - XMPRO App Designer
            • How To Write and Maintain Notes and Business Case - XMPRO Data Stream Designer
            • Interactive 3D Models For Digital Twins - XMPRO Event Intelligence Platform
            • Manage Input Arrow Highlights - XMPRO Data Stream Designer
            • Manage Recommendation Access - XMPRO App Designer
            • Realize Value from End-To-End Condition Monitoring in 6 - 8 Weeks - XMPRO
            • Recommendation Versions - XMPRO App Designer
            • Solution Development Process For Event Intelligence Apps - XMPRO
            • Stream Hosts and How To Install Them - XMPRO Data Stream Designer
            • Use Case Versioning - XMPRO Data Stream Designer
            • XMPRO App Designer Overview - Event Intelligence Applications
            • XMPRO Data Stream Designer - Event Intelligence Applications
            • XMPRO Real-Time Event Intelligence Demo
            • XMPRO Recommendations - Event Intelligence Applications
          • 2019
            • Data Distribution Service: Using DDS in Your IoT Applications
            • My Digital Twin: Digital Twin Applications For Real-Time Operations (Like Me)
            • Setting up a Typical Industrial IoT Use Case with XMPro
            • XMPro Overview & Fin Fan Failure Demo
          • 2016
            • XMPro iBPMS Overview
          • 2013
            • XMPro Best Next Action - 3 Examples for XMPro blog
            • XMPro Case Management Example
            • XMPro Internet of Things Demo
          • 2012
            • Is Agile Business the New Normal
            • The Future of BPM Moving Towards Intelligent Business Operations
            • What industries does XMPro serve-
            • Who is XMPro for-
            • XMPro - The Social Listener - Why You Should Be Listening.wmv
            • XMPro Cool Vendor 2012
            • XMPro iBPMS For SharePoint
            • XMPro iBPMS v6 XMWeb for Intelligent Business Operations
            • XMPro News and Gartner BPM Sydney Summit Discount Offer.mp4
            • XMPro Version 6 - Introducing the Next Generation BPM for Intelligent Business Operations
    • Practice Notes
      • Unified Recommendation Alert Management
      • Performant Landing Pages in Real-Time Monitoring
  • Concepts
    • XMPro AI
      • XMPro Notebook
    • Data Stream
      • Stream Object Configuration
      • Verifying Stream Integrity
      • Running Data Streams
      • Timeline
    • Collection and Stream Host
    • Agent
      • Virtual vs Non-Virtual Agents
    • Application
      • Template
      • Page
      • Block
      • Canvas
      • Page Layers
      • Block Styling
      • Devices
      • Flex
      • Block Properties
      • Data Integration
      • Navigation and Parameters
      • Variables and Expressions
      • App Files
      • Metablocks
    • Recommendation
      • Rule
      • Execution Order
      • Auto Escalate
      • Form
      • Action Requests
      • Notification
      • Recommendation Alert
      • Deleted Items
      • Scoring
    • Connector
    • Landing Pages & Favorites
    • Version
    • Manage Access
    • Category
    • Variable
    • Insights
      • Data Delivery Insights
  • How-To Guides
    • Data Streams
      • Manage Data Streams
      • Manage Collections
      • Use Remote Receivers and Publishers
      • Manage Recurrent Data Streams
      • Use Business Case and Notes
      • Run an Integrity Check
      • Check Data Stream Logs
      • Use Live View
      • Use Stream Metrics
      • Troubleshoot a Data Stream
      • Upgrade a Stream Object Version
      • Setup Input Mappings
      • Use Error Endpoints
      • Use the Timeline
      • Context Menu
    • Application
      • Manage Apps
      • Manage Templates
      • Manage Pages
      • Import an App Page
      • Design Pages for Mobile
      • Navigate Between Pages
      • Pass Parameters Between Pages
      • Page Data
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  5. 2024

Part 4 – Pioneering Progress | Real-World Applications of Multi-Agent Generative Systems

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Part 4 – Pioneering Progress | Real-World Applications of Multi-Agent Generative Systems

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Part 4 – Pioneering Progress | Real-World Applications of Multi-Agent Generative Systems

Part 4 – Pioneering Progress | Real-World Applications of Multi-Agent Generative Systems

Pieter Van Schalkwyk

CEO at XMPRO, Author – Building Industrial Digital Twins, DTC Ambassador, Co-chair for AI Joint Work Group at Digital Twin Consortium

Welcome to Part 4 of our series, where we explore the real-world applications of Multi-Agent Generative Systems (MAGS) in industrial settings. This installment, “Pioneering Progress | Real-World Applications of Multi-Agent Generative Systems,” shifts our focus from theory to the tangible implementation of these advanced systems. Before we dive deeper, let’s briefly revisit where we’ve been

Now, let’s delve into how XMPro leverages its established infrastructure to deploy MAGS effectively, enhancing operational efficiency and adaptability in complex industrial environments.

The Anatomy of an XMPro MAGS Agent

XMPro stands at the forefront of industrial MAGS development, leveraging our existing, robust data pipeline infrastructure. This same foundation, which powers our cutting-edge condition monitoring, predictive operations, and event intelligence solutions, now serves as the bedrock for our MAGS implementation.

Figure 1 shows an Industrial Predictive Maintenance team structured around XMPro DataStreams. This setup features four distinct agents, each with specific roles and functions, which will be detailed in the subsequent sections of this article. This configuration operates without a central “supervising” agent, highlighting our system’s flexibility. Instead, each agent continuously shares and receives updates—observations, reflections, plans, and actions—enhancing collaborative decision-making.

The agents in this example work together to optimize the output of a windfarm while reducing costs and working within resources, time, and budget constraints. This single team can monitor hundreds of wind turbines on the wind farm simultaneously and optimize the overall performance of the system in real-time.

Figure 3 shows the “Anatomy” of the Reliability Agent in the wind farm, which is used in the two examples later in this article. It demonstrates the power and flexibility of XMPro’s DataStreams to compose these agents.

These agents not only communicate with each other but also actively engage with real-time data streams, integrating these inputs into their ongoing processes as per the standard data pipeline architecture discussed in Part 3.

The XMPro DataStream connector framework’s extensibility enables seamless, drag-and-drop integrations across IT, OT, and Engineering systems, providing unparalleled flexibility. Building on this proven architecture, we’ve developed innovative “Agent Brain” components that can be strategically placed within data streams, resulting in fully-featured MAGS agents.

Block 1 represents a standard pattern where industrial data is continually ingested from SCADA, IoT, historians, and other engineering sources. The data is contextualized from business systems, such as a Digital Twin in this example. The XMPro Data Stream can combine, wrangle, and transform this data to ensure the data quality with capabilities such as continuously tracking error rates, completeness ratios, and validity scores.

We do this in Block 2 to enable “human on the loop” oversight and guidance, in case we want to monitor how the agent responds to the information from the analysis in Block 1.

Blocks 1 and 2 represent a typical Predictive Maintenance data stream that monitors specific equipment, runs a predictive model over it, and present prescriptive recommendations to SMEs and other business users.

XMPro has customers that routinely process more than 50 million of these monitoring events per day across a range of complex industrial assets. It is a robust and proven part of the XMPro MAGS agent framework.

Block 3 is the XMPro MAGS extension of the data pipeline and represents the “Agent Brain” that takes the output from the operational data and analytics “function block” and adds to it input from other agents in the team, recent memory on the equipment, and the user or task prompt that is configured for this agent when it is set up. All of this is merged and passed to the XMPro MAGS agent that uses this as an observation, combines it with memories, and reflects on it before planning and coming up with a plan of action.

In this example, the XMPro MAGS agent runs on a local deployment of a Llama model to ensure privacy and security. XMPro is agnostic to the LLM service and can run both cloud and local models, as well as a hybrid of both. Different agents can run on different models that best suit their objectives.

The output of Block 3 determines the actions that the agent can take in Block 4. It could be a recommendation to a human user, but in this example, it updates the preventative maintenance schedules in the Maintenance Management Systems, and it creates a work ticket for a technician to inspect or repair the equipment based on the root causes of failures.

This visual, explainable approach makes it easier to understand the agent’s process and logic. It also makes troubleshooting and fine-tuning much easier, as the output from every step in the data stream can be monitored and evaluated.

Scalability and Governance

The use of a data stream approach further enables scalability and governance that are required for industrial-grade MAGS solutions.

The “stream host” architecture of XMPro DataStreams makes it possible to “instantiate” an infinite number of agents based on an “Agent Profile.” This profile is maintained separately and contains the “Rules of Engagement” that include a system prompt with skills, policies, and deontic rules such as obligations, permissions, authorizations, and delegation of authority. These system prompts always override any user or task prompts to ensure the responsible use of XMPro MAGS.

Figure 9 shows how the XMPro MAGS Predictive Maintenance Team instantiates a team of four agent-based Agent Profiles and then starts observing, reflecting, planning actions, and recoding all this in an XMPro “BrainGraph” memory.

The Reliability Engineering and Root Cause Analysis Agent’s planning and task output are used to create actions based on the plans using the XMPro DataStream Action connectors. Each action and result are further added to the memory to enable learning and continuous self-improvement.

The XMPro MAGS ‘BrainGraph” is an example of a Predictive Maintenance team collaboratively optimizing asset performance through planning and executing corrective and preventative maintenance tasks.

XMPro MAGS Examples

I mentioned the Predictive Maintenance and OEE Expert Optimizer examples earlier, and here is a short summary of these solutions.

Industrial Predictive Maintenance MAGS Team

The Industrial Predictive Maintenance MAGS solution shown in Figure 10 utilizes a team of specialized AI agents working collaboratively to enhance industrial maintenance operations. Each agent plays a pivotal role, ensuring that maintenance is not only reactive but also predictive and strategic, thus minimizing downtime and extending equipment lifespan.

Key agents and their roles within this system include

  1. Reliability Engineering and Root Cause Analysis Agent This agent analyzes equipment performance and failures to improve reliability and conduct thorough root cause analyses. It monitors equipment data to detect patterns that may indicate potential failures, provides detailed root cause analysis reports, and recommends preventive measures to avoid future breakdowns. This agent is based on the same “Agent Profile” as the “Predictive Maintenance Agent” in the OEE Expert Optimizer team.

  2. Maintenance Planning and Scheduling Agent This agent is responsible for efficiently planning and scheduling maintenance activities. It creates predictive maintenance schedules using equipment performance data and historical maintenance records. It aims to optimize these schedules to reduce downtime and improve resource utilization, producing optimized maintenance plans and schedules.

  3. Maintenance Management Oversight Agent This agent ensures that maintenance tasks are executed effectively and in compliance with established standards. It oversees maintenance activities, validates their completion and effectiveness, and enforces maintenance protocols, ultimately ensuring all maintenance activities meet required standards. It doesn’t act as a supervisor that delegate tasks but rather as the process quality assurance.

  4. Reporting and Feedback Agent Focused on transparency and continuous improvement, this agent collects and analyzes data from maintenance activities and equipment performance. It generates real-time Key Performance Indicator (KPI) reports and provides feedback mechanisms to foster ongoing improvements in maintenance operations.

The MAGS workflow of these agents is highly integrated and systematic

  • Data Collection Continuous gathering of equipment performance data, historical maintenance records, and operator input.

  • Reliability Analysis The Reliability Engineering Agent continuously monitors and analyzes equipment to identify and address potential failures.

  • Maintenance Planning The Maintenance Planning Agent develops and refines maintenance schedules based on the analysis.

  • Maintenance Execution Maintenance tasks are carried out as scheduled, and records are duly updated.

  • Management Oversight The Maintenance Management Oversight Agent reviews and validates the maintenance tasks for compliance and efficacy.

  • Reporting and Feedback The Reporting and Feedback Agent provides essential KPIs and actionable insights to ensure continuous optimization of maintenance processes.

By employing this structured approach, the Industrial Predictive Maintenance MAGS solution ensures that maintenance operations are proactive, efficient, and aligned with the organization’s strategic goals, significantly enhancing operational reliability and cost efficiency.

XMPro OEE Optimization Expert team

The Expert OEE Optimizer Multi-Agent Generative System uses an array of specialized AI agents, each tasked with optimizing different facets of manufacturing operations to enhance Overall Equipment Effectiveness (OEE). These agents collaborate in real time, ensuring a highly responsive and adaptive manufacturing environment.

Key Agents and Their Roles

  1. Availability Monitoring Agent This agent manages equipment uptime and downtime data along with maintenance schedules to maximize machine availability. It provides real-time alerts and actionable recommendations to mitigate downtime risks effectively.

  2. Performance Monitoring Agent Responsible for analyzing production speed, cycle times, and operational data, this agent identifies bottlenecks that reduce performance. It offers real-time solutions to enhance production speed and efficiency, thereby optimizing throughput.

  3. Quality Monitoring Agent By assessing quality inspection data, defect rates, and rework records, this agent ensures products meet quality standards. It proactively identifies quality issues, offering solutions to improve product quality and reduce defect rates.

  4. Predictive Maintenance Agent Utilizing equipment sensor data and maintenance logs, this agent predicts potential equipment failures and schedules preventive maintenance. Its goal is to minimize unplanned downtime and optimize ongoing maintenance efforts. This agent is based on the same “Agent Profile” as the “Reliability and Root Cause Analysis Agent” in the Industrial Predictive Maintenance team.

  5. Anomaly Detection and Root Cause Analysis Agent This agent processes historical and real-time operational data to detect anomalies and perform root cause analysis, ensuring swift resolution of performance issues.

  6. Simulation and Scenario Analysis Agent Using synthetic data to represent various operating conditions, this agent simulates potential scenarios to forecast their impact on OEE. The insights generated aid in proactive decision-making and operational optimization.

The MAGS workflow of these agents is highly integrated and systematic

  1. Data Ingestion The system continuously collects data from sensors, maintenance logs, production records, and quality inspections, ensuring a comprehensive data pool for analysis.

  2. Agent Processing Each agent processes the ingested data independently, generating specific outputs such as maintenance alerts, quality reports, and performance metrics.

  3. Collaboration Agents share their outputs to collaboratively refine and enhance operational strategies, leveraging combined insights for a holistic improvement approach.

  4. Solution Presentation The integrated outputs are synthesized into optimized solutions that are presented to human operators and decision-makers, ensuring the implementation of the most effective strategies.

  5. Feedback Loop The implemented solutions are monitored, and the feedback obtained is used to continuously train and improve the agents, enhancing their accuracy and effectiveness over time.

It is still early days in the development of these collaborative, automated MAGS solutions, but for the OEE Expert Optimizer team, Table 1 shows the types of objectives we are setting and measuring. While the objectives differ from factory to factory, they illustrate the potential benefits of a team that observes, plans, acts, learns, and continuously improves with scarce human resources “on” the loop rather than “in” the loop.

By integrating these agents into a cohesive system, the Expert OEE Optimizer not only improves current manufacturing operations but also adapts dynamically to meet future challenges, ensuring sustainable operational excellence and resilience.

Join us on the Journey

We at XMPro are excited about the opportunity that this capability brings. It enables you to augment current skills and address shortages with a framework that mimics the work of your best SMEs. It is not replacing jobs; it is automating suitable tasks so that you can free up scarce SMEs to focus on value-adding work and enable you to do more with less. In the process, you address the productivity challenge mentioned in Part 2.

As systems become more complex, you can use GenAI for what it is good at, processing large volumes of data consistently and helping you to make sense of it, deciding on what action to take next.

Fortunately, the journey doesn’t have to start with full multi-agent MAGS. Maybe you want to start with the Reliability Agent and grow into more functionality and capabilities as your confidence grows with the MAGS approach. XMPro MAGS is a flexible approach that allows you to start small and scale fast.

We are opening limited pilot opportunities for innovative, agile leaders in the industry.

July 18, 2024 – Originallly posted on

In Part 1, “From Railroads to AI The Evolution of Game-Changing Utilities,” I discussed how Generative AI (GenAI) is becoming as fundamental as electricity in our lives, creating opportunities for innovative applications across various sectors.

Part 2, “The Future of Work Harnessing Generative Agents in Manufacturing,” addressed the critical skills shortages and productivity challenges that are reshaping how work is conducted in manufacturing environments.

In Part 3, “AI at the Core LLMs and Data Pipelines for Industrial Multi-Agent Generative Systems,” I covered the architecture of MAGS, focusing on why and how these systems use agents to enhance industrial processes.

In this part of the Agent Data Stream, we can pass all this information to an analytical, statistical, or machine-learning model to provide further insights. In this example, the Reliability Agent runs the information from the DataStream through the for expert analysis of the data. This is “kinda like” giving it a PhD in Reliability Engineering. This step “grounds” the agent in physics and reality. The output of this step is what XMPro typically sends to a real-time dashboard with a recommendation for a human subject matter expert to decide on the next step.

This “Separation of Concerns” is designed to ensure that the XMPro MAGS agents are fit for the tasks assigned to them but behave according to company policies, rules, and regulatory requirements. This is a key area that I will address with ‘s help in part 5.

If this excites you and you want to be part of the next evolution in industrial applications, please reach out to me or , VP Strategic Solutions.

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