Augtera’s Network AI platform prevents incidents and automates operations across data centers, hybrid-cloud, SD-WAN, and public cloud networks. Augtera normalizes data from every possible data source in multi-cloud environments, learns topology, patterns, and correlations at scale, and finds preventive anomalies and correlated incidents. These can be automatically consumed by operators via ticketing systems such as ServiceNow, collaborative tools such as Slack, notification systems, or fed into automation systems to drive dynamic changes.

In this UI user guide, learn how to use the UI, build and manipulate views, create dashboards, and configure basic settings to add or delete users. Detailed information on advanced features, such as data correlation, notifications, and anomaly detection, are planned in future iterations of this document.

Network Model

The Augtera platform automatically discovers network components and represents them in a rich graph. The graphical representation includes objects and relationships among objects, understands multi-level object hierarchies, and identifies peer relationships. It associates metrics, events, and logs learned from time series and logged data with these objects and relationships. The metrics are represented as Measures. Each object is attributed a specific Type, such as a Device, Interface, or Queue. Properties of objects and relationships are represented as Dimensions.


A Dimension can represent a property of an object (ifName of an interface), an identifier, or the property of a related object. Dimensions help organize, view, and filter data. For example, a dimension can uniquely identify if an IP address belongs to a management device, a flow source, or an agent for a probe. The Augtera platform will populate the relevant dimension information for incoming data when it is ingested. If a dimension is selected for a piece of data but does not contain an applicable value, it will be labeled as Null.


The Augtera platform provides a library of measures that define the metrics in the collected data.

When paired with dimensions, measures provide a way to sort and mine data using UI visualizations or query APIs.

  • Stack measures to view multiple measures together. For example, view all ifInDiscards and ifOutDiscards at the same time.

  • Build views, pick measures, and filter on measure values. For example, to view CPU usage that exceeds 90% in heatmaps, specify a minimum value of 90 for the Utilization Measure.

Data Sources

The Augtera software collects data from a wide variety of sources. When data is collected or generated, it is stored as a particular data type. For example, Syslog data is stored in the syslog_v1 data source. To build a view, select single or multiple data sources.

Data Source



Augtera generated anomalies


Augtera generated incidents via topology based auto-correlation


Augtera Internal


Augtera Internal


Interface Metric Data, SNMP traps


Syslog Data


Component + Queue Data


Flow and packet data (IPFIX, Netflow, sFlow, Cloud VPC flow logs)


Synthetic Probe Data (RTT, Loss)

Augtera User Interface

The Augtera user interface contains five top-level tabs: Dashboard, Topology, Anomalies, Incidents, and Trends.

While each tab provides a different function, they also work in conjunction with one another. For instance, the Dashboard tab can display views from Topology, Anomalies, and Trends.


The Dashboard provides information at a glance. At any time, customize the dashboard to present data to address your need. To build a dashboard, simply create and arrange views to gather and display relevant information. For information on building dashboards, refer to Build Dashboards.


The Topology graphically represents the network. Explore the network model that powers the topology and enrich the model with metadata information that defines a group or a role for a device. Use the topology visualization to serve as a real-time interactive representation to detect anomalies, metrics, and events.


The Anomalies table displays a real-time view of all Augtera ML-generated anomalies. When selected, corresponding graphical data and information about the anomaly will be displayed. Create views that isolate various types of anomalies and enable notifications.


Events and anomalies are automatically correlated across multiple data sources using Augtera's topology-aware purpose-built ML algorithm.

For example, if six routers experience BGP flaps, traffic anomalies, and abnormal logs, and Routers 1-5 are all connected to Router 6 (but not to one another), the system will automatically create a single incident. When selected, the contextual topology of the incident will be displayed. Play back the auto-correlated events and anomalies on the topology. Create views that enable notifications on all or a specific combination of correlations.

The topology must be in the system for this to function.

Trends provide a graphical representation of data using Line Charts, Heatmaps, and Tables. Build custom views of the raw data, incidents, and anomalies to analyze when needed. Use the Pinboard functionality (right-hand side of the display) to "pin" top-talkers. Use the left-hand side display of dimensions and the measures to build your views and pinboards. For information on views and pinboards, refer to Use Top Talkers.

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