LiveAction announced the latest release of the LiveNX platform.
The newest features further boost the performance of SD-WANs, offer increased viewability into the network endpoints of all types, and include machine learning and automation, among other enhancements.
LiveNX enables network engineers and IT operations to increase productivity and improve online user experiences by making it easier to monitor, manage, and troubleshoot network performance issues.
The LiveNX platform maximizes a company’s investments in IT network management by ensuring users consistently have optimal online experiences without interruptions or delays. LiveNX performs network diagnostics, monitors service delivery to ensure service level agreements (SLA) are met, automates device discovery to make it easier for users to access the network, and issues proactive alerts to potential problems. It visualizes the entire network and presents a comprehensive view of performance through a dynamic dashboard. LiveNX also integrates with the Cisco Digital Network Architecture (DNA) as well as other multi-vendor environments.
“We strive to provide our customers with the simplest to use, yet most powerful platform for managing and optimizing network performance,” said Walter Scott, CEO, LiveAction. “To do this requires constant feedback from our growing customer base, alignment with our partners to ensure integration with their latest product releases, and the continuous refinement of our platform. With the latest release of LiveNX, customers are assured that their existing network investments will perform alongside the adoption of software-defined WAN solutions, driving down cost and improving end user experiences.”
New features in LiveNX include:
Machine learning and predictive analytics: This includes a “human in the loop” capability incorporates the expertise and understanding of network engineers. The combination of network engineers, machine learning and predictive analytics ensures continuous learning, automation of routine networking tasks, and optimized digital experiences for people and IoT-enabled assets.
Auto semantic device discovery: LiveNX eliminates the time consuming, labor intensive process of manual input of device names, IP addresses, and other critical factors required for network monitoring. The new auto semantic device discovery capabilities automate the input process and consistently learns, making it easy to get up and running with LiveNX while the network continues to get smarter and faster with each use.
Integration with Cisco EasyQoS: Cisco EasyQoS is the Quality-of-Service (QoS) application built into Cisco APIC-EM (EM (Application Policy Infrastructure Controller – Enterprise Module). LiveNX extends the value of Cisco APIC-EM EasyQoS by automating the monitoring and deployment of Cisco IWAN environments.
Monitor of Monitors: Provides an aggregated, global view of the entire network. This includes all devices, links, systems, and activities running on it as well as offering deeper visibility into system health, sites, and network configurations. By maintaining an administrative separation among domains while providing centralized provisioning of users, network engineers and IT operations can more easily identify and troubleshoot issues before they impact the performance of the network.
SD-WAN alerts and notifications: LiveNX’s integration with PagerDuty, the incident management platform, enables administrators to set severity alerts based on hierarchy to notify and escalate issues through email to the proper chain of command. New alert configurations in LiveNX include REST APIs for:
- BGP Peer Connection Change to optimize and ensure the integrity of network traffic routing.
- Interface Percent Utilization to know where and when to make changes.
- Path or routing changes.
Improved report templates: The new templates deliver greater granularity into network performance. They can be categorized by SD-WAN, time series data, voice usage, and the applications most frequently used. They also include flow reports for insight on site-to-site application performance and jitter/loss.
Cloud and endpoint visibility: LiveNX extends visibility for IoT-enabled devices to network operators by capturing network metadata at the edge and the cloud, seeing all devices and actively monitoring the entire network. This agent-based technology now extends to the entire topology, and therefore reduces security risks and optimizes end user experience to all devices.
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.