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HPE Introduces New Mist Agentic AI-Native Innovations

HPE announced major innovations to its HPE Juniper Networking portfolio, advancing its AI-native Mist platform to deliver agentic AIOps through more autonomous, intelligent and proactive network operations. 

New enhancements include agentic AI-powered troubleshooting, expanded visibility and control of self-driving actions, a generalized Large Experience Model (LEM) and new AIOps features for data centers—designed to reduce IT complexity and assure exceptional user experiences from client to cloud.

These new capabilities bolster GreenLake Intelligence, HPE’s next-generation approach to autonomous IT and agentic AIOps, which deploys specialized AI agents within a multi-layered IT architecture. This enables real-time problem-solving, proactive optimization and smarter decision-making across networking, storage and compute. The agentic AI capabilities within Juniper Mist shift IT from reactive to proactive management, laying the groundwork for significant improvements in performance and efficiency.

“Today’s networks must do more than connect—they must understand, adapt and act,” said Rami Rahim, EVP, President and GM, HPE Networking. “With these new digital experience twin and agentic AI capabilities in Juniper Mist, we continue to turn the network into a proactive partner for IT, capable of solving problems before they impact users. This is a major leap toward truly self-driving operations, helping our customers simplify complexity, reduce costs, and deliver exceptional digital experiences at scale.”

HPE Juniper Networking has helped lead the transition to cloud-native, AI-native self-driving operations over the past decade with a unique focus on assuring user experiences from client to cloud. Marvis AI analyzes telemetry across the wired, wireless, WAN and data center domains, and creates automated workflows to simplify operations and lower costs. AI-driven support leverages trouble ticket data to continually train and increase the efficacy of the Marvis AI engine. Plus, a 100 percent API-driven model works with external systems and applications, like Zoom, Teams and ServiceNow to quickly identify and fix the root cause of problems.

Building on these core foundational elements for agentic AI, the latest innovations to the Mist platform bring even more automation insight and assurance to customers and partners:

  • Enhanced conversational capabilities. The Marvis AI assistant has augmented conversational capabilities that facilitate real-time troubleshooting. By leveraging an agentic AI framework, customized insight is provided with self-driving agents that collaborate across the wired, wireless, WAN, client and application domains.
  • Expanded Self-Driving Actions. The Marvis Actions dashboard now supports the autonomous remediation of more network issues, including misconfigured ports, capacity issues and non-compliant hardware—with full IT oversight.
  • Generalized Large Experience Model (LEM). LEM is an AI model unique to HPE Juniper Networking that analyzes billions of data points from applications like Zoom and Teams to easily troubleshoot the performance of common collaboration tools and predict future issues. Now enhanced with Marvis Minis—twins that simulate user experiences—LEM can predict future application experiences without real-time data from the applications themselves. This is fed into the Marvis AI engine where self-driving actions can be taken to optimize future performance, prior to users even being present.
  • AI for Data Center Operations. The Marvis AI Assistant for Data Center integrates with Apstra’s contextual graph database to deliver intelligent insights and lay the groundwork for autonomous service provisioning. Marvis Minis also extends to the data center for continuous service validation and application assurance pertinent to data center networks.

HPE is positioned to unlock exceptional customer value by applying AIOps and agentic AI across multi-vendor full stacks, integrating outcomes from networking, compute, storage, virtualization, containerization, and applications.

The latest Marvis data center capabilities complement HPE OpsRamp, an AIOps-powered IT operations management (ITOM) platform designed to simplify and automate the management of hybrid, multi-cloud, and on-premises IT environments with full-stack observability and advanced agentic workflows tailored for the modern data center.

These innovations build on HPE’s decade-long leadership in AI for networking, helping enterprises, cloud providers and telcos drive greater efficiency, reliability and user satisfaction. 

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HPE Introduces New Mist Agentic AI-Native Innovations

HPE announced major innovations to its HPE Juniper Networking portfolio, advancing its AI-native Mist platform to deliver agentic AIOps through more autonomous, intelligent and proactive network operations. 

New enhancements include agentic AI-powered troubleshooting, expanded visibility and control of self-driving actions, a generalized Large Experience Model (LEM) and new AIOps features for data centers—designed to reduce IT complexity and assure exceptional user experiences from client to cloud.

These new capabilities bolster GreenLake Intelligence, HPE’s next-generation approach to autonomous IT and agentic AIOps, which deploys specialized AI agents within a multi-layered IT architecture. This enables real-time problem-solving, proactive optimization and smarter decision-making across networking, storage and compute. The agentic AI capabilities within Juniper Mist shift IT from reactive to proactive management, laying the groundwork for significant improvements in performance and efficiency.

“Today’s networks must do more than connect—they must understand, adapt and act,” said Rami Rahim, EVP, President and GM, HPE Networking. “With these new digital experience twin and agentic AI capabilities in Juniper Mist, we continue to turn the network into a proactive partner for IT, capable of solving problems before they impact users. This is a major leap toward truly self-driving operations, helping our customers simplify complexity, reduce costs, and deliver exceptional digital experiences at scale.”

HPE Juniper Networking has helped lead the transition to cloud-native, AI-native self-driving operations over the past decade with a unique focus on assuring user experiences from client to cloud. Marvis AI analyzes telemetry across the wired, wireless, WAN and data center domains, and creates automated workflows to simplify operations and lower costs. AI-driven support leverages trouble ticket data to continually train and increase the efficacy of the Marvis AI engine. Plus, a 100 percent API-driven model works with external systems and applications, like Zoom, Teams and ServiceNow to quickly identify and fix the root cause of problems.

Building on these core foundational elements for agentic AI, the latest innovations to the Mist platform bring even more automation insight and assurance to customers and partners:

  • Enhanced conversational capabilities. The Marvis AI assistant has augmented conversational capabilities that facilitate real-time troubleshooting. By leveraging an agentic AI framework, customized insight is provided with self-driving agents that collaborate across the wired, wireless, WAN, client and application domains.
  • Expanded Self-Driving Actions. The Marvis Actions dashboard now supports the autonomous remediation of more network issues, including misconfigured ports, capacity issues and non-compliant hardware—with full IT oversight.
  • Generalized Large Experience Model (LEM). LEM is an AI model unique to HPE Juniper Networking that analyzes billions of data points from applications like Zoom and Teams to easily troubleshoot the performance of common collaboration tools and predict future issues. Now enhanced with Marvis Minis—twins that simulate user experiences—LEM can predict future application experiences without real-time data from the applications themselves. This is fed into the Marvis AI engine where self-driving actions can be taken to optimize future performance, prior to users even being present.
  • AI for Data Center Operations. The Marvis AI Assistant for Data Center integrates with Apstra’s contextual graph database to deliver intelligent insights and lay the groundwork for autonomous service provisioning. Marvis Minis also extends to the data center for continuous service validation and application assurance pertinent to data center networks.

HPE is positioned to unlock exceptional customer value by applying AIOps and agentic AI across multi-vendor full stacks, integrating outcomes from networking, compute, storage, virtualization, containerization, and applications.

The latest Marvis data center capabilities complement HPE OpsRamp, an AIOps-powered IT operations management (ITOM) platform designed to simplify and automate the management of hybrid, multi-cloud, and on-premises IT environments with full-stack observability and advanced agentic workflows tailored for the modern data center.

These innovations build on HPE’s decade-long leadership in AI for networking, helping enterprises, cloud providers and telcos drive greater efficiency, reliability and user satisfaction. 

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...