Microsoft and NVIDIA have expanded their strategic partnership to advance “agentic AI” and “physical AI,” with a focus on enterprise-scale AI agents, robotics, and simulation-driven systems at NVIDIA’s GTC 2026 event in San Jose.
The announcement centres on integrating NVIDIA’s Physical AI Data Factory Blueprint with Microsoft’s Azure cloud network, including Microsoft Foundry, to support large-scale development and deployment of autonomous AI systems.
According to NVIDIA’s official statement, the blueprint is designed to:
“enable massive-scale data processing and curation, synthetic data generation, reinforcement learning and evaluation of physical AI models for vision AI agents, robotics and autonomous vehicles.”
The company also noted Microsoft’s role in the ecosystem, stating:
“Cloud service providers including Microsoft Azure -provide the blueprint to transform world-scale compute into agent-driven turnkey data production engines.”
Microsoft, in its own announcement, said the collaboration is aimed at extending AI capabilities beyond digital applications into real-world systems:
“As AI moves beyond digital experiences, Microsoft and NVIDIA are collaborating to support the next wave of Physical AI.”
The partnership combines Microsoft’s cloud infrastructure and AI platforms with NVIDIA’s accelerated computing and simulation technologies. This includes deeper integration across Azure services and NVIDIA tools used for building digital twins, robotics models, and autonomous AI agents.
Both companies say the goal is to enable “agentic scaling,” where AI systems move beyond simple model outputs to become autonomous agents capable of reasoning, planning, and executing tasks across digital and physical environments.
The partnership’s focus also extends beyond traditional cloud computing and large language models to include agentic systems capable of planning and executing tasks, as well as physical AI applications in robotics, simulation, and autonomous systems. Both companies emphasised that the partnership is designed to scale AI from experimental models into production-grade systems that can operate across complex real-world environments using integrated cloud, compute, and simulation platforms.
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