Enterprise-level network management is becoming increasingly complicated. As companies become more cloud-based, using SaaS solutions and remote workforces, existing network monitoring systems find it difficult to cope. NetOps professionals frequently suffer from fragmented monitoring solutions, Zero Trust encryption, and never-ending individual telemetry alerts.
To deal with the above-mentioned issue, the market leader in observability, Riverbed, presented Network 360 solutions. The new tool offers native Agentic AI technologies in combination with 360-degree network visibility. Integrating the AI solution Riverbed IQ and language assistant Riverbed Q into their core tools such as AppResponse and NetProfiler, Riverbed helps NetOps transition from manual alert handling towards root cause isolation and autonomous network optimization.
By bringing together full fidelity packet capture, enterprise flow analysis, and endpoint information into one single platform, Network 360 enables IT leaders to detect performance issues, forecast potential threats and avoid service interruptions before the end-users experience any problems.
The News: Native Agentic Intelligence and Extended Cross-Domain Reach
The primary architectural advance behind Riverbed’s Network 360 release is integrating autonomous reasoning and natural-language interaction directly into existing NetOps workflows. Rather than requiring engineers to manually query separate packet analyzers and flow databases, the system applies causal, predictive, and agentic AI across all connected operational data.
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Key technological highlights of the announcement include:
Native Integration of Riverbed IQ & Q: Natively embeds Riverbed’s agentic AI engine (IQ) and conversational interface (Q) into AppResponse and NetProfiler, allowing teams to investigate network anomalies using natural language prompts.
Extended Reach via NPM+: Expands visibility across Zero Trust environments, remote endpoints, private data centers, and public cloud infrastructure to eliminate visibility blind spots.
Autonomous Root-Cause Isolation: Synthesizes packet, flow, and endpoint telemetry in real time to isolate the precise source of performance degradation whether caused by cloud latency, local Wi-Fi, or software bugs.
Predictive Anomaly Detection: Leverages machine learning models to identify emerging infrastructure anomalies before they cause network outages or degrade digital employee experiences.
Transforming the IT Operations & Network Observability Industry
Riverbed’s deployment of agentic AI within Network 360 marks a decisive structural shift across the broader IT Operations (ITOps), Network Performance Management (NPM), and Observability sector.
The Sunset of Passive Monitoring and “Dashboard Hopping”
For over a decade, network management vendors competed on dashboard complexity, data ingestion rates, and custom chart builders. However, enterprise IT teams faced acute “dashboard fatigue,” spending valuable time manually correlating graphs from separate software tools during critical network outages.
Riverbed’s announcement highlights the obsolescence of passive monitoring dashboards. The network observability market is entering an execution-first, agentic era. Vendors will no longer be evaluated on how much telemetry they can ingest or display, but on whether their software agents can autonomously correlate evidence, explain root causes, and guide operators through remediation steps.
Establishing “Zero-Trust Compatible Observability” as a Standard
The widespread adoption of Zero Trust Network Access (ZTNA) and encrypted traffic tunnels created significant visibility blind spots for traditional network monitoring tools. Network teams routinely lost visibility once traffic entered encrypted public cloud links or remote worker endpoints.
By extending packet and flow analysis across endpoints and encrypted Zero Trust architectures via NPM+, Riverbed sets a new integration standard. Observability providers must deliver end-to-end operational visibility across cloud, endpoint, and Zero Trust layers without compromising corporate security protocols.
Broad Operational Impact on Enterprise IT Operations
There are several commercial and operational benefits that Chief Information Officers (CIOs), Chief Technology Officers (CTOs), and Vice Presidents of Network Operations can expect when implementing agentic network observability:
Significant MTTR Shortening: Automated data correlation through packets, flows, and endpoints reduces Mean Time to Resolution (MTTR) from hours to minutes, ensuring application availability in a digital environment.
Reduction in Operational Expenses (OpEx): No more time wasted by troubleshooting teams trying to solve the same issue at the network, cloud, and security levels.
Maintenance of Employee Productivity and Digital Experience: Prevention of any potential network issues that can affect system availability and result in disruption in accessing critical SaaS applications for both remote and office-based employees.
Increased Rate of Cloud and Zero Trust Deployments: Allows IT professionals to gain confidence in deploying their workloads in the hybrid cloud environment without losing any network visibility and control.
In the event of unexpected latency in enterprise applications, conventional IT staff spends precious time figuring out which part is responsible for the incident the network, the cloud, or the user device. Implementation of agentic AI observability allows NetOps teams to have an immediate and contextualized understanding of the situation.