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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. 

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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. 

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...