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Juniper Networks Adds Capabilities to Mist AI-Native Networking Platform

Juniper Networks announced key innovations in the Mist™ AI-native networking platform that bring 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 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: 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: 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: 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.

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Juniper Networks Adds Capabilities to Mist AI-Native Networking Platform

Juniper Networks announced key innovations in the Mist™ AI-native networking platform that bring 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 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: 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: 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: 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.

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A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...