Digital Twin Consortium announced that members are developing and deploying Multi-agent GenAI Systems (MAGS) that are redefining the boundaries of how product design, services, and processes can be realized, born of efficiency and optimizations. Use cases include automotive, infrastructure, and manufacturing, where MAGS is utilized to drive significant productivity improvements, streamline operations, and maximize efficiency.
Digital twins are providing advanced levels of automation infused with GEN AI, not only integrating copilots but now utilizing MAGS to perform a multitude of tasks either operating independently, self-organizing, self-optimizing and orchestrated—with or without a traditional human in the loop for decision-making guided by human oversight that is free from conventional repetitive routine activities.
MAGS are composed of multiple interacting GenAI-based agents that perform various tasks, often in parallel. MAGS can now provide decentralized, autonomous, self-organizing, and self-optimizing capabilities. Through interaction with each other and their environment, agents can independently achieve individual or collective goals through reflection, memorization, and continuous improvement.
Infused with Gen AI, each agent can perceive its environment, including multiple modalities, make decisions, and independently act while coordinating and communicating with other agents that may or may not be orchestrated/managed. Some key attributes of a digital twin-based MAGS include interaction, coordination and control, reflection memorization, and execution.
“MAGS provide the next phase of the evolution of digital twin systems and continue to increase business values,” said Dan Isaacs, GM & CTO of Digital Twin Consortium. “Digital twin MAGS are evolving to address challenges such as increasing trusted autonomy and operating with trusted digital twins. Future applications, such as life-critical operations, will require significant testing across many different areas with extensive validation for trustworthiness.”
“XMPro’s work on Multi-Agent Generative Systems (MAGS) extends intelligent digital twin capabilities to include more complex decision-making processes. Early implementations for an Infrastructure Water Utilities Management application have shown potential for enhancing process optimization in real-time,” said Pieter van Schalkwyk, CEO of XMPro. “As this technology evolves, we’re focusing on ensuring its alignment with established safety protocols and ethical guidelines to address the challenges of increased system autonomy.”
“Sev1Tech is leveraging the power of multi-agent generative AI within our advanced Digital Twin platform. This integration allows us to harness the full potential of the digital thread. This comprehensive data framework spans the entire lifecycle of a product,” said Greg Porter, Principal Solution Architect, Sev1Tech. “Our innovative approach is transforming various aspects of our manufacturing operations.”
“SODA has pioneered an advanced multi-agent system that revolutionizes the entire automotive development lifecycle. MAGS can autonomously implement improvements to build times, test efficiency, and resource allocation (at an early stage),” said Sergey Malygin, CEO of SODA. “The system learns from these optimizations, becoming increasingly efficient without human intervention. MAGS seamlessly integrates with Digital Twin technology and Software Defined Vehicle (SDV) approach, creating a dynamic, intelligent ecosystem for automotive innovation that spans from concept to certification and after-sales.
