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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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  • Part 5 – Rules of Engagement: Establishing Governance for Multi-Agent Generative Systems
  • Part 5 – Rules of Engagement: Establishing Governance for Multi-Agent Generative Systems
  • Engagement Rules: Why
  • Engagement Rules: What
  • Engagement rules: How
  • How do we implement the Rules of Engagement in XMPro MAGS
  • How to get started with RoE and MAGS

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  5. 2024

Part 5 – Rules of Engagement: Establishing Governance for Multi-Agent Generative Systems

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Part 5 – Rules of Engagement: Establishing Governance for Multi-Agent Generative Systems

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Part 5 – Rules of Engagement: Establishing Governance for Multi-Agent Generative Systems

In parts of the series, we explored the potential of Multi-Agent Generative Systems MAGS to transform industrial processes. We examined how these systems can enhance decision-making, improve efficiency, and adapt to complex operational environments. As we move forward with implementing MAGS for industrial settings, it’s essential to address a fundamental aspect of their deployment: the rules that govern their behavior.

In this fifth article in the series, Dr. and I will focus on the concept of “Rules of Engagement” for MAGS. These rules serve as the foundation for the responsible and effective use of AI agents in industrial environments. They ensure that our technological advancements align with organizational goals, legal requirements, and ethical standards.

Figure 1 – Position of Rules of Engagement

The need for clear rules becomes apparent when we consider the autonomous nature of MAGS. These systems can make decisions and take actions with minimal human intervention. While this autonomy offers significant benefits, it also presents risks if not properly managed. Establishing robust rules of engagement helps mitigate these risks and builds trust in the technology.

Our discussion will cover various types of rules, including regulatory, organizational, professional, legal, and ethical guidelines. We’ll explore how these rules apply to different entities within a MAGS ecosystem, including human operators, organizations, and AI agentsthemselves.

We’ll also examine practical approaches to implementing these rules. This includes standards-based enterprise modeling and other structuring methods that can define intentions, trust relationships, and interactions between agents and humans. We will explain how these Rules of Engagementare areimplemented in XMPro MAGS.

Let’s begin by examining the fundamental concepts of rules and their importance in complex systems like MAGS.

Engagement Rules: Why

Complex systems consist of diverse actors, each with their own goals, as reflected in their intentions and behavioral preferences. These actors can engage in interactions and collaborations within existing structures, such as organizations, where they accept obligations or commitments associated with their defined roles. They can also form new partnerships with other actors based on mutual agreements.

While internal rules guide the behavior of actors as independent entities and their involvement in collaborations, they must also adhere to external regulations. Both internal and external rules contribute to maintaining system stability, predictability, and trust in an uncertain environment.

AI agents, engaged by humans or organizations, are a type of actor whose behavior can simulate or mimic that of humans. However, ultimate accountability lies with the parties that have legal responsibilities, such as human creators or organizational owners. While AI agents may delegate tasks to other agents, the ultimate responsibility remains with the human or organization. The importance of rules in this context is to clearly define responsibility and accountability across both human and AI agent entities.

We use the term Rules of Engagement to encompass the various types of rules needed to govern the behavior of actors in complex systems. These rules can be used to define obligations, permissions, and prohibitions for actors involved in collaborative activities. They can originate from internal governance arrangements or from external rules.

The latter include regulatory compliance, legal requirements, ethical considerations, professional conduct, engineering and security, safety and risk management, resource management, environmental responsibility, and more.

Engagement Rules: What

When considering computable approaches for supporting engagement rule expressions, the above considerations suggest a need to express:

  • Organizational context that defines rules, whether the context captures a regulatory domain that specifies controlling rules over members of the domain (both individual actors and organisational structures), or a collaboration structure whose objective drives the rules of collaboration.

  • Legal entities that have their own independent life and identity, regardless of their participation in any collaboration

  • Collaboration structures that can define templates for interactions; these allow for multiple instantiations, such as different parties filling roles in such collaboration structures in different times.

  • Organizational or legal policies that cover constraints over behavior of both the actors (parties and AI agents) and collaboration structures, providing guardrails over their autonomous behavior.

  • Range of organisational or legal policy types, including simpler rules associated with obligations, permissions and prohibitions, but also their derivation, needed for more complex expressions of accountability and responsibility (see Figure).

  • Dynamics of the obligations, permissions and prohibitions as a result of authorisation or delegation of services or responsibilities across actors, while ensuring clear traceability of responsibility across the actors

  • Agentic behavior to define possible actions of actors over time, reflecting their objectives while also responding to the actions of others.

  • System policies that can support the variability of system design over time and thus facilitate system evolutions, including the change of organisational or regulative policies.

Engagement rules: How

When building complex systems, the engagement rules should be articulated in a computer interpretable style, to ensureconsistency, interoperability and evolvabilityof the systems, as the operating environment rules change, including the availability of new technologies.

Such computable expressions in turn requires formal modelsto ensure that the computable language can be interpreted using suitable tools, including those that check consistency and traceability from business requirements to implementation, in support of conformance and compliance checking.

One practical approach to implementing such rules is based on formal models embedded in the concepts specifically developed to design and implement large or complex systems, namely the ISO/ITU-T/IEC open distributed processing systems (ODP) standard [1]. One component of the standard, the ODP Enterprise Language provides [2] much of the semantics needed semantic foundationfor describing rules of engagement.

For example, the ODP concept of party, can be used to model actors thar have broad set of responsibilities derived from some social or legal framework, and these can be natural persons or organisations.

Parties may engage automated agents], such as AI agents discussed previously, to perform actions on their behalf, and this involves delegation of their responsibility to address resource issues, either to do with capability specialisation or scalability reasons. The aim here is to be able to trace the way that the rightsand responsibilities of the parties are linked to the individual system actions and their consequences, which is key to ensuring building responsible digital twin systems.

Further, the ODP concept of community is useful to define the collaboration structures introduced earlier, which serve as templates for interactions of actors participating in collaborations. A community is essentially a contract specifying how actors can participate in different community roles to fulfil their objectives, and that of the community’s objective. The contract is expressed in terms of obligations, permissions or prohibitionspolicies that apply to the actors fulfilling these roles.

These policies, often referred as deontic policies, are intended to capture the rules of the community contract, including those that propagate to the community, from the outer regulatory environment. Note that the term “Deontic” has roots in philosophy and logic,specifically in the study of obligation, permission, and prohibition and in recent AI developments they are employed to design systems that adhere to rules and regulations, such as access control mechanisms, legal frameworks, and ethical guidelines for AI [3].

The ODP-EL standard provides a practical implementation path for handling these deontic and accountability concepts, through encapsulating them within objects that can be handed over across the actors in the system, referred to as deontic tokens [2]. Here, the holders of these tokens, are constrained by the nature of policies encapsulated within them. This is similar mechanism as used in many recent security policy approaches such as OAuth 2.0 access tokens, although the constraints here are broader and apply to any holder’s actions rather than to narrower, data access actions, in access control policies.

Further, deontic tokens can be associated with some important actions performed by actors, when they result in changes of obligations, permissions or prohibitions associated with the actors.

These actions are referred to as speech acts, introduced following the linguistic concepts of speech act, and they can be used to express how certain actions such as requests, orders or promises, change the state of world, from the perspective of obligations or permissions of actors involved. They are powerful way of expressing chain of responsibility across parties and their AI agents.

This foundational model for expressing engagement rules as constraints on expected behaviour of agents, also serves as a basis for the inclusion of related modelling concepts/formalism and techniques needed for analysing and reasoning about system properties related to AI agent technologies.

One such technique, referred to as Promise Theory [4], is suitable for describing intention of AI agents and how these can be related to their objectives, future actions, and decisions, and thus the deontic formalism mentioned above. Such decisions can be influenced by agents own objectives and commitments, while allowing for potential choices associated with expected behaviour and trust in other agents with which they can interact and collaborate.

How do we implement the Rules of Engagement in XMPro MAGS

Here is an example from the oil and gas industry. A major refinery uses XMPro MAGS to manage its operations, from production planning to maintenance scheduling and safety monitoring. The system includes various AI agents responsible for different aspects of the refinery’s operations, each needing to work in harmony with others while adhering to strict industry regulations.

Figure 3 – Agent Profile and Agent Instance

Agent Profile System Prompts serve as the foundation for establishing overarching rules in XMPro MAGS. These prompts include

Organizational rules:These are high-level directives that reflect the company’s values and policies. For example:

"Prioritize safety in all operations" 
"Comply with environmental regulations at all times" 
"Optimize resource utilization to minimize waste" 
"Maintain product quality standards across all production lines" 

Deontic rules:These specify what agents must, may, or must not do in various situations. Examples include

"Agents must report any safety anomalies immediately" 
"Production agents may adjust output levels within specified ranges" 
"Maintenance agents must not schedule repairs during peak production hours unless critical" 

To implement these rules flexibly and dynamically, XMPro MAGS uses a system of Deontic Tokens. These tokens are based on concepts from the ISO/ITU-T/IEC open distributed processing systems (ODP) standard, ensuring a standardized approach to rule implementation. Each token represents a specific rule, including its type, the subject it applies to, and the conditions for its application.

The XMPro MAGS Agent Memory Cycle integrates these tokens at every stage of an agent’s decision-making process:

  1. Observation:Tokens guide what information agents can access and how they interpret it. For instance, a safety monitoring agent might be authorized to access real-time sensor data from all parts of the refinery.

  2. Reflection:Agents consider applicable tokens when evaluating past actions and forming new insights. This allows them to learn from experience while staying within defined boundaries.

  3. Planning:Token-based rules influence the creation and prioritization of action plans. A production planning agent might adjust its scheduling strategy based on recent performance data and current operational constraints.

  4. Execution:Before any action, agents verify compliance with relevant tokens. This ensures that even in dynamic situations, all actions align with organizational rules and regulations.

This approach ensures that Rules of Engagement actively shape agent behavior in real-time. It allows the refinery to maintain safe, efficient, and compliant operations while adapting to changing conditions. For example, system administrators can quickly update the relevant tokens through the Agent Profile if a new environmental regulation is introduced.All affected agents will immediately adjust their behavior to comply with the new rule, without reprograming each agent individually.

By implementing Rules of Engagement through Agent Profile System Prompts and Deontic Tokens, XMPro MAGS balances consistent agent behavior with operational flexibility. This method offers several benefits:

  • Ensures AI systems remain aligned with business goals and regulatory requirements

  • Provides a clear audit trail of decision-making, crucial in highly regulated industries

  • Allows for quick adaptation to changing rules or conditions

  • Maintains consistent behavior across different agents and processes

  • Enables fine-grained control over agent permissions and restrictions

  • Facilitates easier updates to system-wide policies without extensive recoding

The system also supports hierarchical rule structures, allowing for both broad, organization-wide rules and specific, task-level rules. This hierarchical approach ensures that agents can handle complex, nuanced situations while still adhering to overarching principles.

In the refinery example, this might look as follows:

  • Top-level rules about safety and environmental compliance

  • Department-level rules about production targets and quality standards

  • Task-specific rules for individual processes or equipment

This layered approach to Rules of Engagement helps organizations implement AI systems that are both powerful and controllable, suitable for complex industrial environments like oil and gas refineries. It provides the flexibility needed to optimize operations while maintaining the strict control necessary in high-risk, highly regulated industries.

How to get started with RoE and MAGS

As we’ve explored in this article, implementing Rules of Engagement is essential for the successful deployment of Multi-Agent Generative Systems MAGS in industrial environments. These rules, when properly designed and executed, help organizations balance AI autonomy with necessary controls. XMPro’s approach, using the Deontic Token System, offers a practical way to manage AI agent behavior while maintaining operational flexibility. This method allows companies to adapt quickly to changing regulations and business needs without compromising on safety or compliance.

For senior managers and technical leaders considering MAGS for their operations, understanding these concepts is crucial. The potential benefits of MAGS are significant, but so are the challenges of implementation. Every organization will have unique requirements for their Rules of Engagement, based on their industry, risk profile, and strategic goals.


References

[1] Linington, P.F., Milosevic, Z., Tanaka, A., Vallecillo, A.: Building Enterprise Sys- tems with ODP: An Introduction to Open Distributed Processing, 1st Edition. Chap- man&Hall/CRC Innovations in Software Engineering and Software Development (2011)

[2] ISO/IEC IS 15414, Information Technology – Open Distributed Processing – Enterprise Language 3rd edn, 2015. Also published as ITU-T Recommendation X.911.

[4] Burgess, M., & Bergstra, J. A. (2014). Promise theory: Principles and applications. Springer.

Previous articles

Our demonstrate how AI agents are becoming an important element of complex systems, addressing the resource scarcity, data complexity and unlocking of new economic values.

(* adapted from an upcoming conference publicatiob by )

uses a structured approach to implement Rules of Engagement for AI agents.. This framework ensures AI agents operate within defined boundaries and follow organizational policies. It also helps agents make decisions that align with ethical standards and business objectives, which is crucial in complex industrial settings.

If you’re interested in exploring how Rules of Engagement and Deontic systems could work for your company, we encourage you to take action. Dr. and are available for in-depth discussions about applying these concepts to your specific situation. Their experience can help you navigate the complexities of AI governance in industrial settings. To arrange a consultation, please reach out through our website or LinkedIn profiles. We look forward to helping you shape the future of your operations with responsible AI implementation.

[3] Z. Milosevic, Ethics in Digital Health: a deontic accountability framework, Proceeding of EDOC2019 conference,

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