RESOURCES · LIBRARY
Explore operating pressure, trusted context, decision architecture, governance, autonomy progression, implementation, and industry use cases.
Find the right resource for your stage — defining the operating path, building trusted context, governing decisions, proving readiness, or scaling bounded autonomy.
The Digital Twin Consortium has published the Industrial AI Agent Manifesto — a governance framework for deploying AI agents in safety-critical industrial environments. Ten engineering laws for trustworthy autonomous operations.
Open resource →
The Distributed Connector Pattern: Why Where You Query Matters More Than How Fast Part 1 of a series on scaling data-intensive applications Coming in 2026: The distributed connector architecture...
Open resource →
Download XMPro’s definitive guide to Multi-Agent Generative Systems (MAGS) — a practical roadmap for industrial leaders who want AI that actually works in real operations, not just pilots....
Open resource →
The Ultimate Guide to Industrial AI: Doing It Right at Scale A practical playbook for doing Industrial AI right at scale, built on XMPro's composable architecture and Multi-Agent Generative Systems....
Open resource →
Agentic AI Readiness Assessment Framework for Engineering Executives
Open resource →
Introduction: Bridging the Gap Between AI Potential and Operational Reality The promise of GenAI Virtual Assistants is undeniable: automation at scale, real-time decision-making, and personalized...
Open resource →
A technical paper explaining why industrial AI agents need governed decision architecture, not only reasoning models. Introduces the Cognitive Decision Loop, ORPA, scored gates, Decision Trace and bounded actuation as an architecture for industrial agent control.
CAN’T FIND WHAT YOU NEED?
If the resource you need isn’t here yet, tell us — we’ll route it to the right person or scope something purpose-built.
