The company’s expanded data platform aims to cut AI token consumption and accelerate network operations as enterprises move towards autonomous IT.
Enterprise AI may have a model problem, but increasingly, it has a data problem. As organisations race to deploy copilots and AI agents, the quality and context of the information fed into these systems is emerging as a critical factor determining whether AI delivers reliable outcomes or expensive mistakes.
NETSCOUT is addressing this challenge by expanding its data platform around what it calls Smart Data high-fidelity, contextualised operational evidence derived directly from observed digital interactions.
The company argues that traditional metrics, events, logs and traces often force AI systems to reconstruct what actually happened after data has been sampled, aggregated and fragmented across multiple tools. That process can consume significant compute resources and AI tokens while increasing the possibility of inaccurate conclusions.
“Unlocking the benefits of AI across the enterprise will not be achieved by adding another model. It will succeed through context engineering giving AI the right operational context before reasoning begins,” said Sanjay Munshi, Chief Operating Officer, NETSCOUT.
According to NETSCOUT’s internal testing, its context-rich approach delivered more than a 25% reduction in AI token consumption compared with MELT-only data, alongside a more than 75% reduction in mean time to knowledge (MTTK).
The strategic significance extends beyond cost optimisation. As AI agents move from assisting IT teams to potentially taking autonomous actions, enterprises will require more reliable, explainable and independently observed operational evidence.
NETSCOUT’s architecture focuses on extracting semantic meaning early from network packets and optimising context at the source. The result is designed to provide AI systems with denser, more relevant information without overwhelming their context windows.
For enterprises navigating the journey from AIOps to AgenticOps, the message is increasingly clear: better AI may not simply come from larger models. It may come from giving those models better data, better context and stronger operational guardrails.
