NETSCOUT gives AI assistants direct access to real-time network intelligence

Last Updated 1 day ago by Kenya Engineer

NETSCOUT Systems has introduced Model Context Protocol (MCP) connectivity to its Omnis™ AI Insights solution, a move that allows AI assistants, models and agents to access the company’s network intelligence on demand.

Announced on September 22, the capability is built around NETSCOUT’s AI-ready Smart Data, which is derived from its real-time network monitoring technology. The company says the approach is intended to give AI systems access to contextual, operational evidence that can be used to support more accurate and explainable decisions.

The development reflects a broader shift in enterprise networking towards providing artificial intelligence with more than raw telemetry. As organisations increasingly deploy AI for IT operations, observability, cybersecurity and service assurance, the quality and context of the data available to AI systems can become as important as the AI models themselves.

From raw telemetry to contextual intelligence

NETSCOUT’s approach is based on its Adaptive Service Intelligence™ (ASI) technology, which captures detailed information about applications, services, transactions and network behaviour.

Rather than sending all of that information downstream in its original form, NETSCOUT says its platform performs semantic extraction and context optimisation close to the point where the data is generated. The resulting Smart Data is intended to retain the operational meaning of the original information while being substantially more compact and suitable for use by analytics and AI systems.

Two components of the Omnis AI Insights solution play a central role.

Omnis Sensor performs semantic extraction at network vantage points, identifying application, service, transaction and behavioural context in real time. It generates metadata while preserving what NETSCOUT describes as the operational meaning of the underlying network events.

Omnis Streamer then collects and curates the resulting Smart Data for downstream applications. Organisations can use configurable playbooks to shape datasets according to particular operational requirements and industries, with templates available for environments including healthcare, financial services and telecommunications.

The Streamer can also make the resulting information available directly to AI assistants and agents through its newly integrated MCP server.

MCP

Model Context Protocol has emerged as a mechanism for connecting AI models and agents to external tools and sources of information in a structured way. Rather than requiring an AI application to be separately engineered for every data source, MCP can provide a standardised way for an AI system to discover and use available tools and contextual information.

For network operations, this creates the possibility of an AI assistant querying live operational evidence when investigating an incident rather than attempting to reach a conclusion from a static knowledge base or incomplete monitoring information.

NETSCOUT says its MCP implementation provides AI assistants and agents with access to relevant Smart Data at runtime, with NETSCOUT-provided tools helping direct the AI system towards the evidence required for a particular task.

The company argues that this can also reduce the amount of raw information that must be processed by an AI model. By performing data preparation and contextualisation earlier in the pipeline, organisations can potentially reduce processing requirements, token consumption and the complexity of downstream data infrastructure.

Connecting existing monitoring environments

The new MCP capability is not intended to replace the other ways organisations can consume NETSCOUT data.

Smart Data can already be integrated into a range of analytics and operational platforms, including Splunk, ELK Stack, Datadog, ServiceNow and Dynatrace. NETSCOUT says the new capability therefore adds another route through which its network intelligence can be consumed, rather than requiring organisations to abandon existing monitoring or data workflows.

The company also points to investment protection as part of the proposition. Omnis Sensor Adaptors can be used to extend the capabilities of existing NETSCOUT infrastructure.

This could be significant for organisations that have already invested in network monitoring but are now looking to introduce AI-driven operations without building entirely new data pipelines.

Giving AI evidence rather than inference

The distinction between raw telemetry and contextual evidence is particularly important when AI is being used for operational decisions.

NETSCOUT cites a live deployment in which conventional application monitoring tools reported no application errors requiring investigation even as network conditions were degrading the user experience.

According to the company, NETSCOUT Smart Data retained detailed evidence of what was happening on the network, including the minimum window size, total number of retransmissions and zero-window events. Such information could allow an AI system investigating the incident to verify the underlying network conditions rather than simply infer a likely cause from incomplete application-level information.

The distinction is increasingly relevant as organisations move from AI-assisted analysis towards more autonomous IT operations. An AI system tasked with diagnosing an outage or initiating a remediation action requires reliable operational evidence if its decisions are to be trusted.

Phil Gray, AVP of product management at NETSCOUT, said the company had designed Omnis AI Insights to give organisations flexibility in how they supply AI systems with network intelligence.

“Everyone knows there is no value to conclusions that cannot be trusted,” Gray said. He added that the combination of MCP tools and existing Kafka streaming capabilities allows customers to provide AI-ready Smart Data to analytics and AI platforms while also making the same contextual information directly accessible to models and agents.

Towards more autonomous network operations

The introduction of MCP connectivity comes as enterprises increasingly explore AI agents capable of moving beyond answering questions to investigating incidents, correlating information across systems and potentially taking operational actions.

That evolution places greater emphasis on the data foundation supporting AI. Poorly contextualised or fragmented data can force AI systems to make inferences from incomplete information, while excessive volumes of raw telemetry can increase processing costs without necessarily improving the quality of the resulting decision.

NETSCOUT’s proposition is therefore not simply to connect an AI model to more network data, but to prepare the evidence before it reaches the model.

For network operators and large enterprises, this could provide another route towards integrating AI into observability, service assurance, cybersecurity and IT operations while retaining existing network-monitoring investments.

The company says Omnis AI Insights can make its contextual network intelligence available across AI, analytics, observability, security, service assurance and data-lake environments without requiring organisations to re-platform their existing infrastructure or construct new data pipelines.

The development ultimately reflects a changing role for network infrastructure itself: from systems that merely generate telemetry to infrastructure capable of producing structured, contextual information that can be consumed directly by increasingly autonomous software systems.