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Juniper Delivers Expanded Client-to-Cloud Insights and Self-Driving Network Operations

Juniper Networks announced key innovations in the Mist AI-native networking platform , bringing expanded insight and assurance to wired , wireless and WAN customers and partners. Enhanced Marvis Minis extends digital experience twinning across the global WAN, reaching into both public and private cloud environments and applications. A new Marvis Actions self-driving dashboard simplifies network operations by seamlessly identifying and resolving network issues and continuously optimizing network experience and performance, without manual operator intervention, and an enhanced Marvis mobile client expands Mist's industry-leading AI-native Operations (AIOps) to end user devices. With these latest innovations in Mist, Juniper continues to lead the industry in AIOps from client-to-cloud, giving operators superior visibility and control of user experiences. 'The Mist AI-native networking platform was purpose-built to converge AI and networking for exceptional operator and end user experiences,' said Sudheer Matta, Senior Vice President, Products, Campus and Branch, Juniper Networks. 'These enhancements shift the paradigm from traditional observability to an AI-native model for truly understanding user experience that's actionable at scale. We think of the new Marvis Minis as a million Minis—digital experience twins working in unison to proactively identify, learn and act before issues impact the user. With Marvis Minis, Juniper continues to deliver state-of-the-art automation, insight and assurance—setting the stage for a foundational shift to agentic AI in the networking industry.' Client-to-cloud experience twinning: The next frontier for Marvis Minis With this enhancement, Marvis Minis digital experience twinning capabilities now proactively analyze user experiences end-to-end, from client-to-cloud to baseline and pinpoint exactly where application performance may be suffering. Marvis Minis now offers new service level expectations (SLEs) to deliver increased visibility into application performance across various levels, such as at site, across sites, regions, within an ISP, making troubleshooting faster and more efficient. With end-to-end monitoring, Marvis Minis now provides a comprehensive solution for identifying and resolving issues before they impact end user experience. Unlike traditional observability tools that require agents, sensors or customer-side deployment, Marvis Minis offers a fully seamless experience powered by AI. Marvis Actions dashboard: Visualizes and controls self-driving network operations Aligned with Juniper's vision of self-driving networks, Marvis AI Assistant proactively resolves network issues like VLAN misconfigurations and network loops, optimizes Radio Resource Management (RRM) and automates routine tasks such as policy updates firmware compliance, increasing overall efficiency. The new Marvis Actions dashboard view gives full control over when and how these self-driving network operations are enabled. It also provides a detailed history of all proactive actions, whether fully self-driving or assisted, along with insights into how Marvis AI Assistant identified and resolved each issue, empowering customers to manage their network on their terms. Marvis Client: Empowering IT teams with client-side data Marvis Client , an enhanced Marvis AI Assistant extension, uses client-side telemetry from Android, Windows and macOS devices to provide deeper insights into user experiences. Rich data such as device type, operating system, radio hardware, firmware and connectivity metrics are transmitted in near real-time to the Mist cloud, where Marvis AI Assistant processes it to generate actionable insights. When these insights are further complemented by data collected from Juniper Access Points (AP), routers, switches and firewalls, IT teams can proactively address performance issues, improve troubleshooting and enable a consistently high-quality user experience. All of this is achieved without the need for additional software or hardware sensors, thereby minimizing cost and complexity and maximizing value. 0 0