NETSCOUT SYSTEMS announced its recent participation in TM Forum’s NeuroNOC Catalyst, an innovation project at DTW Ignite 2025.
TM Forum is a global alliance of telco and tech companies, leading the industry in defining the building blocks for new operating models, impactful new partnerships, and advanced software platforms. The NeuroNOC Catalyst project explored how AI agents, closed-loop automation, and high-quality network data can enable self-healing operations across telecom environments.
Developed in collaboration with global leaders, including Amazon Web Services, Accenture, Symphonica, and Sand Technologies—and championed by carriers such as BT Group, Telecom Argentina, Omantel, Turknet, Axian Telecom, and Safaricom—the NeuroNOC initiative demonstrated how AI and automation can dramatically accelerate fault detection and resolution in live network environments.
NETSCOUT deployed its Omnis AI Insights Solution, consisting of Omnis AI Sensor and Omnis AI Streamer, to deliver 5G Standalone Radio Access Network (SA RAN) and Packet Core high-fidelity telemetry essential for effective AI-driven operations. Omnis AI Sensor utilizes NETSCOUT’s signature deep packet inspection (DPI)-based, end-to-end network visibility, and Omnis AI Streamer provides powerful analytics and filtering at source via an open API driven dataset. In simulated service-impact scenarios, the solution enabled network operations center (NOC) engineers to identify subscriber registration issues, pinpoint the root cause via a curated large language model (LLM), and execute remediation steps with minimal manual effort.
The key takeaway was strong empirical validation of the need for high-quality curated data to drive effective AI solutions. Without data quality, AI models are hollow shells incapable of delivering valuable results, and data curation can unleash great power to ensure better outcomes. Moreover, the project showed promising results, including an expected 80% reduction in manual troubleshooting and up to 50% lower operational costs for communications service providers. It further reduced data usage and tokenization by AI models like AWS Bedrock by up to 80%, pointing to additional potential savings.
“Accurate, real-time curated data is the foundation of intelligent network operations. Without high-quality packet collection across the network, it’s nearly impossible to correlate issues across multiple data streams, determine root causes, and verify and test automated fixes,” said Richard Fulwiler, a Catalyst participant and Senior Director, Product Management at NETSCOUT. “While fully autonomous networks are still in their early stages, this project shows the powerful potential of AI agents – armed with the right data in real-time – to support faster and more accurate resolution of network issues.”
The Latest
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 ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...