Ask a cybersecurity pro about Network Detection and Response (NDR) and you might still hear "Noisy," "Too much data." But ask the teams running NDR that includes agentic AI capabilities and you'll hear they're actually using it to catch threats earlier, triage faster, and chase fewer false positives. The old complaint lingers in part because reputations are sticky, and because NDR has evolved faster than the narrative. NDR deployments have always given analysts deep visibility into network traffic, encrypted session behavior, and protocol anomalies. But visibility often came as raw material, not finished intelligence. Some systems required extensive manual tuning during deployment to prevent SIEM overload. Organizations that couldn't invest that time (or didn't know how important it was) helped cement NDR's "alert firehose" or "noisy" reputation. Agentic AI autonomously fetches data, triages alerts, and performs correlation and initial analysis, handling the time-consuming, repetitive work that used to bury analysts. Here's the unexpected twist: the data volume that once could overwhelm teams if the NDR wasn't appropriately tuned, has become a strategic asset. Because AI can ingest and simultaneously analyze thousands of data points, "noise" can become rich ground for finding actionable signals such as connections between low-severity, informational, or otherwise low profile activity most SOC teams would never have the capacity to piece together. The system can surface detections that might otherwise have been missed. With AI processing data volume and tedious tasks, analysts are freed up to focus on the top threats. NDR with agentic AI pieces together a complete, correlated story from network data and surfaces a prioritized set of detections such as an anomalous connection tied to a failed login, a suspicious DNS query, or unusual file access. Each detection is delivered with the network evidence analysts need for immediate context. NDR should still be tuned t