The Digital Twin Consortium (DTC) has announced that its members are developing and deploying Multi-agent Generative AI Systems (MAGS), which are redefining the boundaries of product design, services, and processes through enhanced efficiency and optimisation.
These systems are being utilised across various sectors, including automotive, infrastructure, and manufacturing, to drive significant productivity improvements, streamline operations, and maximise efficiency.
Digital twins, now integrated with Generative AI, are providing advanced levels of automation. MAGS perform a multitude of tasks independently, self-organising, and self-optimising. These systems can operate autonomously or under human oversight, freeing individuals from repetitive routine activities.
MAGS consist of multiple interacting AI-based agents that perform various tasks, often simultaneously. These agents offer decentralised, autonomous, self-organising, and self-optimising capabilities. By interacting with each other and their environment, they achieve individual or collective goals through reflection, memorisation, and continuous improvement.
Each AI agent, infused with Generative AI, can perceive its environment, make decisions, and act independently while coordinating with other agents. Key attributes of digital twin-based MAGS include interaction, coordination and control, reflection, memorisation, and execution.
“MAGS provide the next phase of the evolution of digital twin systems and continue to increase business values,” said Dan Isaacs, GM and 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.”
“SODA has pioneered an advanced multi-agent system that revolutionises 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 optimisations, 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.”
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