Riverbed Launches Network 360 Integrating Agentic AI into Enterprise Observability

On September 16, 2026, Riverbed introduced its Network 360 intelligent observability suite, bringing native agentic AI capabilities from Riverbed IQ and Q directly into its AppResponse and NetProfiler platforms. Alongside these AI integrations, the company expanded NPM+ telemetry capture across public cloud workloads, zero trust network access (ZTNA) architectures, and distributed remote environments to provide continuous, end-to-end network visibility. This release tackles a critical pain point in enterprise infrastructure management: the growing complexity of hybrid environments where NetOps teams must maintain performance across networks they do not directly control. As workloads span multiple clouds, CDNs, and SaaS dependencies, traditional passive telemetry generation creates alert fatigue without actionable context. By embedding agentic AI models that evaluate telemetry in real time, the platform aims to automatically identify root causes, correlate anomalous traffic spikes across disparate hops, and suggest remediation pathways instead of relying on engineers to manually stitch together disparate network evidence. This development reflects a broader architectural shift across the observability and AIOps landscapes. The industry has rapidly progressed from descriptive dashboards to predictive and agentic workflows. As modern cloud-native architectures grow increasingly nondeterministic—compounded by distributed microservices and autonomous AI agents communicating over complex API meshes—traditional threshold-based monitoring is insufficient. Leading observability platforms are actively consolidating causal AI, deep telemetry collection, and autonomous reasoning agents into unified operational control planes to lower Mean Time to Resolution (MTTR). In practice, engineering and operations teams should evaluate how agentic network observability fits into their existing incident response pipelines. While automated root-cause identification reduces diagnostic toil, teams should maintain human-in-the-loop validation for any automated remediation actions. Furthermore, organizations adopting zero trust and multi-cloud strategies should review their telemetry ingestion budgets and ensure that expanding packet-level and flow-level visibility across perimeter edges does not create cost overheads or compliance challenges with unencrypted payload inspection.