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InfoVista Launches VistaInsight Service Assurance Platform

InfoVista announced the launch of a real-time and dynamic service assurance platform for its Service Assurance solution, VistaInsight.

Communications Service Providers (CSPs) can benefit from VistaInsight to accelerate their virtualization (NFV) programs through a real-time view of the network, support for closed-loop automation, and enrichment of Big Data.

CSP networks are undergoing a transition from legacy physical components to a new, virtualized infrastructure. During this transition, there will be a lengthy period during which complex, hybrid physical and virtualized networks will co-exist. This hybrid network environment demands speed and agility in the provisioning and deployment of new services in both the virtualized and traditional environments.

If a Service Assurance system does not keep up with the speed of service provisioning, is not synchronized with the changes in network topology, and cannot ingest and enrich data from multiple sources, CSPs will be unable to deliver and maintain the velocity required to meet business demands. To achieve such levels of service in both virtualized and non-virtualized networks, Service Assurance systems need to be open, agile, and highly responsive.

VistaInsight Real-time and Dynamic Service Assurance platform accelerates virtualization programs by exposing the platform capabilities to the entire CSP ecosystem. This is achieved through an extensive set of open APIs, telemetry sourcing of data, dynamically synchronized state of the network topology, and a real-time view of network and service performance. By streaming data in real-time, VistaInsight is dedicated to increasing business agility and interoperability in both virtualized and non-virtualized networks.

VistaInsight’s open API capability is key for the success of both the design- and run-time of upcoming NFV hybrid networks, and for designing NFV hybrid service models. Through its real-time dynamic service modelling, support for zero-touch operations, and support for wider and deeper analytics, CSPs can obtain immediate business benefits.

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InfoVista Launches VistaInsight Service Assurance Platform

InfoVista announced the launch of a real-time and dynamic service assurance platform for its Service Assurance solution, VistaInsight.

Communications Service Providers (CSPs) can benefit from VistaInsight to accelerate their virtualization (NFV) programs through a real-time view of the network, support for closed-loop automation, and enrichment of Big Data.

CSP networks are undergoing a transition from legacy physical components to a new, virtualized infrastructure. During this transition, there will be a lengthy period during which complex, hybrid physical and virtualized networks will co-exist. This hybrid network environment demands speed and agility in the provisioning and deployment of new services in both the virtualized and traditional environments.

If a Service Assurance system does not keep up with the speed of service provisioning, is not synchronized with the changes in network topology, and cannot ingest and enrich data from multiple sources, CSPs will be unable to deliver and maintain the velocity required to meet business demands. To achieve such levels of service in both virtualized and non-virtualized networks, Service Assurance systems need to be open, agile, and highly responsive.

VistaInsight Real-time and Dynamic Service Assurance platform accelerates virtualization programs by exposing the platform capabilities to the entire CSP ecosystem. This is achieved through an extensive set of open APIs, telemetry sourcing of data, dynamically synchronized state of the network topology, and a real-time view of network and service performance. By streaming data in real-time, VistaInsight is dedicated to increasing business agility and interoperability in both virtualized and non-virtualized networks.

VistaInsight’s open API capability is key for the success of both the design- and run-time of upcoming NFV hybrid networks, and for designing NFV hybrid service models. Through its real-time dynamic service modelling, support for zero-touch operations, and support for wider and deeper analytics, CSPs can obtain immediate business benefits.

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...