Juniper Networks Inc.‘s Mist platform, was purpose-built with AI in mind, leveraging automation and insight to optimize user experiences. Built into Mist is the Marvis AI engine and Assistant, which uses high quality data, advanced AI and machine learning data science, and a conversational interface to simplify deployment and troubleshooting. Now under Hewlett Packard Enterprise Co., Mist has been brought together with Aruba Networks to form what they are calling the “secure AI-native network,” which is a blend of leading AIOPs, product breadth and security to solve real customer and partner needs. Ultimately the company has a vision of using the platform to bring all HPE Networking products under common cloud management and AI engine with centralized operations. “One thing that we added is the ability to choose specific areas for self-driving mode that don’t require human intervention,” said Jeff Aaron, vice president of product and solution marketing at HPE. “If a switch port is stuck or an AP is running non-compliant software, for example, you can tell Marvis to go fix it on its own. We provide reporting to show which features were fixed autonomously, how they were fixed, and why the decision was made so IT still has complete visibility into what is happening.” In addition, Marvis got a back-end upgrade, leveraging more generative AI capabilities and agentic workflows for even better real-time troubleshooting. The assistant has always used natural language processing and understanding to understand simple language queries and provide insightful answers on par with human experts. Furthermore, Marvis’ AIOps capabilities have been expanded further into the data center through tighter integration with Juniper Apstra’s contextual graph database. This allows Marvis to analyze infrastructure configurations and provide answers to data center-related inquiries using the same Marvis conversational interface employed elsewhere in the network. Finally, HPE Networks also expanded their ability to proactively predict and prevent video issues using what it calls a large experience model or LEM. This pulls in billions of data points from Zoom and Microsoft Teams clients and correlates it with networking data to identify the root cause of video issues. The LEM framework has now been augmented with data from Marvis digital experience twins, or Minis, which probe the wired, wireless, WAN and data center networks autonomously when users aren’t even present to provide even richer data for predictive and proactive troubleshooting. The impact shows up in different ways across industries. ServiceNow reported a 90% reduction in network trouble tickets, while Blue Diamond Growers cut the time spent managing networks by 80%. Gap achieved 85% fewer truck rolls, and Bethesda Medical reported 85% faster upgrades.