Juniper Networks announced a new round of innovations for its routing portfolio that expedite deployment and enhance routing troubleshooting at scale.
By leveraging Mist AI, Juniper’s platform for AI-native operations, Juniper’s WAN routing solution has been enhanced with even more automation and insight for end-to-end routing observability and control. The new Juniper Networks® ACX7020 Access Edge Router, also launched today, extends these automation capabilities all the way to the metro and customer premises. With Juniper’s enhanced AI-Native Routing portfolio, enterprises, cloud and service providers alike can unleash the potential of their WAN investments to deliver unparalleled performance and reliability and minimize carbon footprint, meeting the demands of the AI era.
The Juniper® Paragon portfolio makes automating the WAN intuitively easy, from Day 0 to Day 2+. It reduces deployment times while ensuring that both network operations teams and end users have consistently amazing experiences. As a key solution within the Juniper AI-Native Networking Platform, Paragon has been enhanced with the following capabilities:
- AI-native routing observability: Mist AI now provides comprehensive visibility and control of end-to-end routing using AI Operations (AIOps). This helps to detect complex routing issues and anomalies and proactively recommend actions. By introducing new levels of insight and automation to network operators, Mist AI has been proven to reduce ongoing expenditures by up to 85 percent in some instances.
- Proactive troubleshooting with LLM Connector: In addition to leveraging Marvis®, Juniper’s leading Virtual Network Assistant (VNA) driven by Mist AI, LLM Connector gives customers the option of leveraging their own LLM (large language model) deployments for advanced conversational interactions. This enables straightforward troubleshooting to accelerate resolution of even the most complex routing issues.
- Intent-based network optimization: Juniper now supports fully intent-driven traffic engineering policies that streamline and simplify routing optimization to meet the performance requirements of the applications that use the network. This builds on the established knowledge inherent to Juniper WAN solutions, created via decades of sophisticated deployments that involve many terabytes of traffic and thousands of optimizations each month.
Juniper's comprehensive portfolio of best-of-breed routers offer unparalleled capacity, agility and operational consistency with the end-to-end automation required for service-aware networks that power today’s hyperconnected world. The Juniper AI-Native Routing solution has been augmented with the following new capabilities:
- Reduced energy bills and consumption with a new energy-efficient automation use case: Juniper leverages port, port group and line card sleep features within its routing platforms to scan, identify and switch off any quantity of modules that are not required to support current traffic demand.
- Extended WAN coverage into customer premises with the ACX7020 Access Edge Router: The ACX7020 is an I-Temp-rated access router deployable in both indoor and outdoor environments and features a compact form factor for installation in space-constrained locations. With this router, network operators can deliver exceptional performance by connecting endpoints directly to the WAN, with plug-and-play support for AI-Native Routing.
The Latest
For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...
FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...
Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...
While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...
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 ...