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Juniper Networks Brings AIOps to WAN Routing

Juniper Networks announced another round of innovation to its premier AI-Native Networking Platform, driving exceptional value and cost savings to enterprises requiring secure branch connections.

The company’s WAN Assurance, Premium Analytics and Marvis® Virtual Network Assistant (VNA) products have been augmented with new and unique AI for Networking capabilities that deliver simple, seamless and secure SD-WAN and SASE experiences.

Juniper has also announced a new Routing Assurance product that is the first in the industry to bring AI-Native automation and insight to traditional edge routing topologies.

With these latest platform enhancements, Juniper offers a single AI-Native Platform that reduces operational expenditures, by up to 85 percent in some instances, across the entire networking footprint.

The augmented Juniper solution leverages AI for Networking to drive even more value to enterprise WAN environments:

- Assured SD-WAN experiences with proactive AIOps - Marvis Minis, Juniper’s digital experience twin solution that improves network ops by diagnosing real authentication issues without requiring users/devices, has been extended to SD-WAN. New WAN speed tests can be continuously run (without users having to be present) to verify link speeds and take proactive actions if problems are detected. With this latest Marvis Minis expansion, Juniper is the first vendor to span wired, wireless and WAN with a single AI-Native digital experience twin solution, enabling exceptional end-to-end user experiences. In addition, existing WAN service level expectations (SLEs) for WAN edge health, link health and application health have been augmented with a new SLE that tracks WAN congestion. The new WAN Congestion SLE alerts operators when their network interfaces are being over-utilized, which causes poor user experiences. Juniper has also further expanded its unique streaming dynamic packet capture (dPCAP) solution for wireless and wired to now include WAN. With WAN dPCAP, the Juniper WAN Assurance solution proactively captures packets at the time of a bad incident to help identify and fix hard-to-find issues, avoiding expensive and time-consuming site visits. Finally, new application insights offer network operators a user-friendly visualization of the traffic traversing the SD-WAN, enabling them to see bandwidth-intensive applications and enable accurate planning and problem remediation.

- Integrated SSE/SD-WAN (SASE) insight via expanded premium analytics dashboard - Juniper is introducing a new security insights Mist dashboard within its Premium Analytics product to provide comprehensive security event visibility and persona-based policy activation and threat responses. This increased visibility provides actionable intelligence to security teams, enabling them to quickly identify incidents and respond to threats in real-time—thereby improving the user experience. The security insights dashboard in Premium Analytics also helps break down siloed network and security management. Networking and security teams benefit from a shared portal that shows proactive actions needed (and taken), which streamlines operational workflows for increased efficiency, agility and cost savings.

- First AI-Native WAN routing solution - Another innovation announced by Juniper, Routing Assurance, brings the company’s high performance, sustainable and versatile enterprise edge routing platforms under the Mist AI and cloud umbrella. With Juniper Mist Routing Assurance, Juniper is modernizing the WAN edge with customizable service levels that allow administrators to monitor, analyze and resolve issues and anomalies identified by Mist AI swiftly across connected WAN Edge and peering locations for edge MX and ACX routing products, including MX204, MX304 and ACX7024 models. In addition, Marvis, an AI-Native VNA with a conversational interface built on more than seven years of learning, has been expanded to cover enterprise WAN edge routing. With Marvis’ conversational interface, IT teams can use simple language queries to identify and fix routing issues, including knowledge base queries powered by Generative AI.

With these latest expansions to its platform, Juniper is provides a single AI-Native and cloud-native solution that spans all key networking domains—from campus and branch to data center and all WAN links in between (regardless of topology).

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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.

Juniper Networks Brings AIOps to WAN Routing

Juniper Networks announced another round of innovation to its premier AI-Native Networking Platform, driving exceptional value and cost savings to enterprises requiring secure branch connections.

The company’s WAN Assurance, Premium Analytics and Marvis® Virtual Network Assistant (VNA) products have been augmented with new and unique AI for Networking capabilities that deliver simple, seamless and secure SD-WAN and SASE experiences.

Juniper has also announced a new Routing Assurance product that is the first in the industry to bring AI-Native automation and insight to traditional edge routing topologies.

With these latest platform enhancements, Juniper offers a single AI-Native Platform that reduces operational expenditures, by up to 85 percent in some instances, across the entire networking footprint.

The augmented Juniper solution leverages AI for Networking to drive even more value to enterprise WAN environments:

- Assured SD-WAN experiences with proactive AIOps - Marvis Minis, Juniper’s digital experience twin solution that improves network ops by diagnosing real authentication issues without requiring users/devices, has been extended to SD-WAN. New WAN speed tests can be continuously run (without users having to be present) to verify link speeds and take proactive actions if problems are detected. With this latest Marvis Minis expansion, Juniper is the first vendor to span wired, wireless and WAN with a single AI-Native digital experience twin solution, enabling exceptional end-to-end user experiences. In addition, existing WAN service level expectations (SLEs) for WAN edge health, link health and application health have been augmented with a new SLE that tracks WAN congestion. The new WAN Congestion SLE alerts operators when their network interfaces are being over-utilized, which causes poor user experiences. Juniper has also further expanded its unique streaming dynamic packet capture (dPCAP) solution for wireless and wired to now include WAN. With WAN dPCAP, the Juniper WAN Assurance solution proactively captures packets at the time of a bad incident to help identify and fix hard-to-find issues, avoiding expensive and time-consuming site visits. Finally, new application insights offer network operators a user-friendly visualization of the traffic traversing the SD-WAN, enabling them to see bandwidth-intensive applications and enable accurate planning and problem remediation.

- Integrated SSE/SD-WAN (SASE) insight via expanded premium analytics dashboard - Juniper is introducing a new security insights Mist dashboard within its Premium Analytics product to provide comprehensive security event visibility and persona-based policy activation and threat responses. This increased visibility provides actionable intelligence to security teams, enabling them to quickly identify incidents and respond to threats in real-time—thereby improving the user experience. The security insights dashboard in Premium Analytics also helps break down siloed network and security management. Networking and security teams benefit from a shared portal that shows proactive actions needed (and taken), which streamlines operational workflows for increased efficiency, agility and cost savings.

- First AI-Native WAN routing solution - Another innovation announced by Juniper, Routing Assurance, brings the company’s high performance, sustainable and versatile enterprise edge routing platforms under the Mist AI and cloud umbrella. With Juniper Mist Routing Assurance, Juniper is modernizing the WAN edge with customizable service levels that allow administrators to monitor, analyze and resolve issues and anomalies identified by Mist AI swiftly across connected WAN Edge and peering locations for edge MX and ACX routing products, including MX204, MX304 and ACX7024 models. In addition, Marvis, an AI-Native VNA with a conversational interface built on more than seven years of learning, has been expanded to cover enterprise WAN edge routing. With Marvis’ conversational interface, IT teams can use simple language queries to identify and fix routing issues, including knowledge base queries powered by Generative AI.

With these latest expansions to its platform, Juniper is provides a single AI-Native and cloud-native solution that spans all key networking domains—from campus and branch to data center and all WAN links in between (regardless of topology).

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.