Riverbed Adds Agentic AI to Network 360 to Automate Hybrid Telemetry and Diagnostics

On September 16, 2026, Riverbed unveiled its updated Network 360 portfolio, embedding native agentic and causal AI capabilities—powered by Riverbed IQ and Q—directly into its core network observability platforms, AppResponse and NetProfiler. In tandem, the release expands distributed data collection via NPM+, extending traffic capture and flow analysis across public cloud workloads, remote worker devices, and zero-trust network access (ZTNA) overlays. For enterprise network operations (NetOps) and site reliability engineering (SRE) teams, this announcement addresses one of the most persistent bottlenecks in modern infrastructure management: manual metric correlation. While traditional telemetry tools capture massive volumes of packet-level data, flows, and SNMP metrics, engineers still bear the burden of assembling disparate clues during major incidents. By introducing agentic AI workflows, the platform analyzes deep network evidence across distributed environments, correlates symptoms with probable underlying causes, forecasts emerging capacity or routing anomalies, and surfaces contextual next-step remediation workflows. This development reflects a broader architectural evolution taking place across cloud and infrastructure observability. As organizations transition from centralized on-premises data centers to distributed hybrid clouds and SaaS models, blind spots multiply. Software-defined perimeters and ephemeral cloud networking render static threshold alarms and traditional perimeter inspection insufficient. The industry is responding by converging telemetry collection with autonomous agents—shifting the purpose of observability platforms from displaying metrics on dashboards to driving proactive diagnosis and automated system resilience. In practice, engineering leaders should evaluate how agent-assisted network telemetry can streamline triage and reduce mean time to resolution (MTTR) within multi-tier environments. However, operations teams must recognize that AI-generated diagnostic recommendations remain contingent on the fidelity of underlying data streams. NetOps practitioners adopting agentic network tools will need to establish robust validation gates, ensuring automated insights align with established change management policies before granting autonomous systems direct remediation authority over routing and network configuration changes.