NETSCOUT unveils an expanded data platform that provides trusted operational context for enterprise AI across observability, service assurance, cybersecurity, and AI workflows.

Internal testing shows more than a 25% reduction in AI token consumption versus MELT-only data and over a 75% improvement in mean time to knowledge.

The platform couples early semantic extraction at the observation point with context optimization at the source to deliver higher-density, relevant evidence with less compute.

It addresses gaps where traditional MELT data can be incomplete or lack context, which can hinder reliable AI decisions and drive up inference costs.

Market context notes from Gartner and IDC emphasize the importance of semantics, real-time context, and trusted data environments for agentic AI and autonomous actions.

Benefits include boosted productivity for NetOps/SecOps/DevOps/SRE, reduced token costs through denser signals, and greater automation confidence from independently observed, explainable evidence.

The platform observes digital interactions, converts packets into high-fidelity, contextualized evidence in real time, and curates this evidence at scale to support AI-ready decisions.

An executive quote from NETSCOUT COO emphasizes context engineering as a cornerstone of successful AI adoption and autonomous operations.

Smart Data is embedded across NETSCOUT solutions and can be integrated into enterprise AI workflows, ecosystems, and operations teams.

The platform aims to enable governed automation and agentic workflows across observability, cybersecurity, cloud and data center transformation, and resilience in hybrid environments.

NETSCOUT is expanding its data foundation from deep packet inspection to a broader AI-ready operational intelligence layer for enterprise automation and AI.