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NETSCOUT Adds MCP Connectivity, Putting AI-Ready Smart Data Directly Into the Hands of AI Assistants and Agents

NETSCOUT expands Omnis AI Insights so AI assistants can directly consume curated, context-rich network data via MCP and existing tool integrations.

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Built on NETSCOUT's Data Platform, Omnis AI Insights Gives On-Demand, Streaming, and Integrated Access to Trusted Network Evidence

WESTFORD, Mass.--(BUSINESS WIRE)-- NETSCOUT SYSTEMS, INC. (NASDAQ: NTCT), a leading provider of observability, AIOps, cybersecurity, and DDoS attack protection solutions, today announced Model Context Protocol (MCP) connectivity for its Omnis™ AI Insights solution. The new capability gives AI assistants and agents on-demand access to AI-ready Smart Data, NETSCOUT’s real-time operational evidence, providing the trusted context they need to support more accurate and informed decisions.

AI-ready Smart Data builds on NETSCOUT’s patented Adaptive Service Intelligence™ (ASI) technology, using granular data to provide a richer, more scalable source of contextual network intelligence. NETSCOUT performs early semantic extraction and context optimization at source, transforming ASI data into compact, AI-ready Smart Data before it enters downstream systems. Omnis Sensor and Omnis Streamer are key components of NETSCOUT’s Omnis AI Insights solution, moving intelligence closer to the source of the data and enabling network infrastructure to evolve from simply producing telemetry to delivering contextual, AI-ready network intelligence.

  • Omnis Sensor performs early semantic extraction at critical network vantage points to represent application, service, transaction, and behavioral context within ASI in real time at the source. It generates essential metadata while preserving operational meaning at the point of observation. This compact, high-fidelity evidence gives AI models trusted operational context for more accurate, efficient, and explainable decisions.
  • Omnis Streamer collects and curates AI-Ready Smart Data for downstream use. Customizable playbooks shape the data for any domain, operational requirement, or use case, with healthcare, financial services, and telecommunications service provider environments among the available templates. It delivers those datasets through platform integrations, or on demand to AI assistants and agents through its new, built-in MCP server.

Enriching network data before it reaches an AI model reduces the volume, cost, and complexity of processing raw telemetry while giving AIOps, observability, security, and analytics systems more meaningful evidence for faster, more reliable decisions and increasingly autonomous operations.

Extending the Value of the NETSCOUT Data Platform

Omnis AI Insights provides organizations with a ground truth, evidentiary view of operations that AI agents and assistants require for trusted autonomous action:

  • On-demand access for AI: MCP connectivity gives AI assistants, AI models, and agents relevant AI-Ready Smart Data at run time. NETSCOUT-provided tools guide them to the evidence that matters and help them interpret it.
  • Direct platform integration: Ingestion of Smart Data via integration by Splunk, ELK Stack, Datadog, ServiceNow, Dynatrace and others.
  • Investment protection: Omnis Sensor Adaptors help customers add these capabilities to their existing NETSCOUT infrastructure.

AI-Ready Datasets: Clean Inputs, Confident Answers

“Everyone knows there is no value to conclusions that cannot be trusted,” said Phil Gray, AVP, product management, NETSCOUT. “By adding MCP tools alongside our existing Kafka streaming capabilities, Omnis AI Insights gives IT professionals the flexibility to feed AI-ready Smart Data into analytics and AI platforms at scale and cost effectively, while also making that same context-rich intelligence directly accessible to Models and Agents. This helps organizations power AI with a compact, curated, trusted source of network truth rather than fragmented operational signals that suffer from hallucinations and high token spends.”

In a live NETSCOUT deployment, conventional application monitoring tools indicated no application errors and nothing to investigate, while underlying network conditions degraded the user experience. NETSCOUT Smart Data preserved exactly what happened across the network, including the minimum window size, total retransmit count, and zero-window event count, allowing AI to verify facts rather than infer reality.

NETSCOUT AI-ready Smart Data helps customers reach accurate answers faster, reduce token and infrastructure costs, and advance toward governed autonomous operations with greater confidence. It also extends the value of existing NETSCOUT investments while providing a differentiated data foundation for future AI innovation. These new capabilities in the Omnis AI Insights solution put trusted operational context to work across AI, analytics, observability, service assurance, security, and data lake environments without re-platforming or building new data pipelines.

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About NETSCOUT

NETSCOUT SYSTEMS, INC. (NASDAQ: NTCT) protects the connected world from cyberattacks and performance and availability disruptions through its unique visibility platform and solutions powered by its pioneering deep packet inspection at scale technology. As a leading provider of network observability, AIOps, carrier service assurance, cybersecurity, and Distributed Denial-of-Service (DDoS) attack protection solutions, NETSCOUT serves the world’s largest enterprises, service providers, and public sector organizations. Learn more at www.netscout.com or follow @NETSCOUT on LinkedIn, X, or Facebook.

©2026 NETSCOUT SYSTEMS, INC. All rights reserved. Third-party trademarks mentioned are the property of their respective owners.

Editorial Contacts:

Chris Lucas
NETSCOUT Systems, Inc.
+1 978 614 4124
chris.lucas@netscout.com

Chris Shattuck
Finn Partners for NETSCOUT
+1 404 502 6755
NETSCOUT-US@FinnPartners.com

Source: NETSCOUT SYSTEMS, INC

Key Terms

model context protocol technical
A model context protocol is a set of rules or guidelines that determine how a financial model interprets and applies information within a specific situation. It helps ensure consistent and accurate analysis by clarifying what data or assumptions are relevant in a given scenario. For investors, it provides clarity on how predictions or assessments are made, increasing confidence in decision-making.
aiops technical
AIOps (Artificial Intelligence for IT Operations) uses machine learning and data analysis to monitor, detect, and resolve problems in an organization’s technology systems automatically. It matters to investors because it can cut downtime and operating costs, speed up fixes, and make digital products more reliable—similar to an autopilot that notices and corrects issues before they disrupt service, which can protect revenue and reduce operational risk.
telemetry technical
Telemetry is the automatic collection and transmission of measurements from remote devices, systems, or patients to a central system for monitoring and analysis—like a car sending engine, speed and location data back to a dashboard. For investors it matters because telemetry provides real-time evidence of product performance, safety and user behavior, helping assess revenue potential, operational risk, regulatory compliance and whether a product is meeting market demand.

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