Latest news with #XMPro
Associated Press
6 hours ago
- Business
- Associated Press
XMPro Named as a Sample Vendor in Gartner® Report: Customer Trust Is a Critical Barrier to Agentic AI Adoption
Leading Industrial AI Platform Recognized for Reliability Tools that Build Trust in Agentic AI Systems 'Our hybrid approach to agentic AI, combining advanced LLM models with proven industrial AI techniques, directly addresses the trust challenges that organizations face when deploying autonomous systems'— Pieter Van Schalkwyk DALLAS, TX, UNITED STATES, June 9, 2025 / / -- XMPro, a leading provider of industrial AI and intelligent business operations solutions for asset-intensive industries, today announced it has been recognized as a Sample Vendor in the Gartner® report, 'Emerging Tech: Customer Trust Is a Critical Barrier to Agentic AI Adoption' published on June 2, 2025.* XMPro was recognized as a Sample Vendor. According to the report, 'Gartner research reveals that a top inhibitor to agentic AI adoption is a lack of customer trust. Vendors that offer observability tools, reliability controls and explainability features will emerge as near-term winners in the agentic AI market.' According to the report: 'Many interviewed providers did not use GenAI for task execution; rather, they used classical AI technologies, such as ML models and rule-based logic. For example, using LM for reasoning but ML for task execution. This hybrid approach to agentic AI embeds transparency and reliability into task automation.' 'We believe, our inclusion in this Gartner report validates our focus on building trusted industrial AI solutions that deliver real operational value,' said Pieter van Schalkwyk, CEO of XMPro. 'Our hybrid approach to agentic AI, combining advanced language models with proven industrial AI techniques, directly addresses the trust challenges that organizations face when deploying autonomous systems in critical industrial environments.' Industry Context: The Trust Challenge in Agentic AI The Gartner report reveals critical insights about agentic AI adoption: • 'The study interviewed 20 agentic AI providers, of which a majority (more than 50%) cited customer trust as a top challenge to driving customer adoption.' • 'By 2028, less than 10% of agentic AI deployments will operate unsupervised, up from less than 1% in 2025' • 'the market is currently favoring semisupervised, simple task automation over more autonomous, complex agentic task automation.' XMPro's Approach to Trusted Industrial Agentic AI Through Composite AI: XMPro's intelligent business operations solution (iBOS) addresses the trust challenges identified in the Gartner report through its unique Composite AI framework that combines six complementary AI methodologies: •Truth-Grounded Architecture: Every AI recommendation passes through multiple validation layers including first-principles validation, symbolic rule enforcement, evidentiary reasoning, and multi-agent cross-checks, ensuring decisions are safe, explainable, and trusted •Hybrid AI Integration: Combines Generative AI for insight synthesis with Symbolic AI for rules-based intelligence, First Principles Models for physics-based validation, and Causal AI for root-cause discovery, addressing the report's emphasis on combining language models with classical AI •Agentic AI with Bounded Autonomy: Orchestrates coordinated teams of specialized AI agents that observe, reason, plan, and act, with configurable human oversight and flexible autonomy controls based on operational risk tolerance •Real-Time Industrial Observability: Built-in monitoring, tracing, and alerting mechanisms designed specifically for mission-critical industrial environments where equipment failure or safety incidents have major consequences •Domain-Specific Industrial Intelligence: Pre-configured for aerospace & defense, manufacturing, mining, oil & gas, utilities, and other asset-intensive industries, translating domain expertise into formal logic structures with clear, auditable reasoning chains •Multi-Agent Collaboration with Guardrails: Supports collaborative multi-agent teams that work together on complex industrial problems while enforcing safety protocols and operational constraints Organizations can learn more about XMPro's industrial agentic AI solutions at *Source: Gartner, 'Emerging Tech: Customer Trust Is a Critical Barrier to Agentic AI Adoption,' Danielle Casey, Alfredo Ramirez IV, Anushree Verma, Akhil Singh, Aakanksha Bansal, 2 June 2025 Gartner Disclaimer: Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. About XMPro XMPro is a leading industrial AI company that helps enterprises achieve measurable business outcomes through intelligent operations. The company's Multi-Agent Generative Systems (MAGS) platform combines industrial digital twins with trusted agentic AI to optimize operations, improve asset performance, and enhance decision-making in complex industrial environments. XMPro serves Global 2000 companies in manufacturing, mining, energy, utilities, and other asset-intensive industries, helping them navigate their industrial AI transformation journey with confidence. Wouter Beneke XMPro email us here Visit us on social media: LinkedIn Facebook YouTube X Legal Disclaimer: EIN Presswire provides this news content 'as is' without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
Yahoo
05-05-2025
- Business
- Yahoo
XMPro MAGS 1.5: Agentic AI for Industry with MCP & A2A Integration
Bringing Anthropic's MCP and Google's A2A into the factory — with industrial-grade governance and safety boundaries DALLAS, May 05, 2025 (GLOBE NEWSWIRE) -- XMPro today announced the release of Multi-Agent Generative System (MAGS) version 1.5, introducing an advanced trust architecture for industrial AI that establishes new standards for reliability, security, and cross-domain collaboration. The update features evidence-based confidence scoring, multi-method consensus decision-making, standardized agent-to-agent communication, and seamless AI integration through Model Context Protocol (MCP). The new capabilities directly address the critical challenges industrial organizations face when deploying AI systems in environments where reliability, safety, and performance are non-negotiable requirements. Learn more at Watch Introductory Demo here → XMPro's Collaborative AI Agent Teams For Industrial Operations Watch the Deep Dive Demo here → Collaborative AI Agent Teams for Autonomous Industrial Operations Agent-to-Agent (A2A) Communication Protocol XMPro MAGS v1.5 implements Google's Agent-to-Agent (A2A) protocol as a communication framework that transforms how AI agents interact across organizational boundaries. A2A establishes a common language for AI agents from different providers to communicate while respecting different trust requirements. "The A2A protocol integration bridges the traditional divide between operational technology and information technology," said Gavin Green, VP of Strategic Solutions at XMPro. "Organizations can now maintain high-trust standards for industrial systems while enabling controlled collaboration with business domains that may operate under different requirements." XMPro's implementation uses a layered approach: A2A DataStream Connector: Enables no-code configuration of agent communication, maintaining XMPro's visual approach to agent design Protocol Bridge: Translates between XMPro's existing MQTT/OPC US/DDS/Kafka-based communication and A2A's JSON-RPC format Agent Card Capabilities: Each agent exposes its capabilities through a digital identity that describes what it can do and how to authenticate with it "What distinguishes industrial AI from general business applications is the need for absolute trust in automated systems that can affect physical operations," said Pieter van Schalkwyk, CEO at XMPro. "With XMPro MAGS 1.5, we've created a comprehensive trust architecture that gives industrial organizations the confidence to deploy AI at scale, while maintaining appropriate boundaries between operational and business domains." Evidence-Based Confidence Scoring MAGS 1.5 introduces a sophisticated confidence assessment framework that evaluates agent observations, reflections, plans, and actions using five key dimensions, including evidence strength, consistency analysis, reasoning quality assessment, uncertainty quantification and stability measurement. The system combines these factors using configurable weights to produce normalized confidence scores categorized into various confidence levels, allowing organizations to set appropriate thresholds for different types of decisions based on criticality. Multi-Method Consensus Decision-Making MAGS 1.5 introduces an advanced consensus framework that enables agent teams to make better decisions together. This system combines: Collaborative Iteration: Agents work through structured rounds of proposal and conflict resolution rather than simple voting Intelligent Conflict Detection: Automatically identifies resource contentions and interdependencies between agent plans Adaptive Protocols: Dynamically selects appropriate decision methods based on situation complexity Expertise Weighting: Gives greater influence to agents with relevant domain expertise Confidence Integration: Adjusts validation requirements based on confidence scores Smart Escalation: Routes low-confidence decisions to humans with comprehensive context Complete Traceability: Captures all proposals, conflicts, and justifications for audit purposes This system reduces decision bottlenecks, improves plan quality, and creates the right balance between agent autonomy and human oversight—enabling teams to tackle complex challenges with greater reliability and transparency. Model Context Protocol (MCP) Integration MAGS 1.5 incorporates the Model Context Protocol (MCP) developed by Anthropic as a standardized access layer for AI models to interact with external data sources and tools. In industrial settings, MCP functions as a "translator" that allows AI models to effectively leverage contextual data through three key capabilities including tools, resources and prompts. XMPro has implemented MCP Action Agents as DataStream connectors, enabling direct integration of MCP-compliant tools within real-time data processing workflows. Control and Governance at Scale Underlying these advancements is XMPro's architectural approach that uses DataStreams as control envelopes for AI agents. This creates a fundamental separation between agent reasoning and action execution, establishing safety boundaries that don't depend on perfect agent behavior. "In industrial environments, you can't rely solely on an agent's internal constraints," explained van Schalkwyk. "Our approach allows organizations to deploy sophisticated AI capabilities while maintaining rigorous control over what actions can be executed in their operational environments." AI agents in the XMPro MAGS framework can observe data, reflect on patterns, and develop action plans, but they cannot directly execute these actions. Instead, all proposed actions must pass through DataStream control mechanisms that evaluate them against predefined rules and constraints. This separation ensures safety isn't compromised even if an agent's reasoning produces inappropriate recommendations. Strategic Benefits of the Trust Architecture The comprehensive trust architecture in MAGS 1.5 delivers significant strategic benefits for industrial organizations: OT/IT Integration: The longstanding challenge of bridging operational technology and information technology is addressed by enabling a heterogeneous but interoperable ecosystem where each domain maintains its appropriate level of rigor. Organizational Coherence Without Compromise: Different parts of the enterprise can work in concert while respecting their distinct trust requirements, eliminating the need to force a single standard across domains with different reliability needs. Selective Trust Boundary Control: Industrial organizations can maintain high-trust operational systems while selectively exposing capabilities to business functions through well-defined interfaces. Human-AI Collaboration Model: The system identifies when human review is needed, creating an effective collaboration framework where humans remain in control of critical decisions. Future-Proofing Across Domains: As the AI agent landscape evolves toward specialization, the standards-based approach positions XMPro MAGS to participate in broader agent ecosystems while maintaining industrial-grade & Reasoning Diagram – XMPro Collaborative AI Agent Teams For Autonomous Industrial Operations Building on Successful Hannover Messe 2025 Showcase Earlier this month, XMPro successfully showcased its MAGS framework at Hannover Messe 2025 as part of an integrated demonstration with Dell Technologies. The well-received demonstration highlighted how collaborative AI agent teams can address complex industrial challenges without requiring extensive data science expertise or specialized IT infrastructure. Availability MAGS version 1.5 is available immediately for existing customers and will be available to new customers starting May 15, 2025. For more information, visit or contact sales@ About XMPro helps industrial companies rapidly build intelligent operations solutions using composable AI, digital twins, and real-time data streams. Our platform enables collaborative AI agent teams to monitor, reason, and act—turning complex data into actionable intelligence. Learn more at A photo accompanying this announcement is available at
