Marvis Virtual Network Assistant
The first AI-driven Virtual Network Assistant (VNA) service that transforms how your IT teams interact and engage.

Client-to-Cloud View Via Actions: Journey to a Self-Driving Network

Marvis Actions is about driving operational simplicity and transforming IT from reactive troubleshooting to proactive remediation. It offers a “morning cup of coffee” view, which delivers visibility into high-impact network issues at an organizational level so that administrators know exactly what they need to prioritize and focus on for the day. As sites get added, Marvis Actions scales with ease as no additional setup is required from the user.

Marvis proactively identifies the root cause of issues across IT domains (WLAN, LAN, WAN, security, and applications), providing insights into every high-impact issue seen when a client is accessing an application either in the data center or cloud (client to cloud). It can either automatically resolve issues (self-driving mode) and in the future with permissions from the user, or it recommends actions that require user intervention (driver assist mode). Once completed, Marvis closes the feedback loop by validating that the actions are correct in the Mist AI engine, thus helping Marvis learn while gaining your IT team’s trust.

For Juniper Mist Wired Assurance, some Marvis Actions are listed here. Marvis isolates wired clients failing to connect, and it identifies bad network cables connected to ports, mismatched port settings, L2 loops, DHCP failure scopes, wired clients failing to authenticate, and continuous port flaps with an option to also disable the port that the user can ask Marvis to automate in the future.

It also automatically adds missing VLAN tags, fixes incorrect port mode configuration, and isolates persistently failing wireless clients. It also identifies missing VLANs from third-party switches. For Juniper Mist™ Wi-Fi Assurance, Marvis Actions helps isolate APs that missed a firmware upgrade in any given site, provides root causes and failure scopes when an AP goes offline (site or switch down scenario), isolates persistently failing wireless clients, detects coverage holes and APs or locations with insufficient capacity, identifies APs connected to a bad cable, and detects missing VLANs. Another unique capability is providing insights into issues with upstream services and devices such as radius, DHCP, DNS servers, and ARP gateways. Root cause analysis not only includes server failures, but also includes complete failures seen on an AP, switch, WLAN or across a site or organization.

Additionally, Marvis realizes our AI-driven support with proactive return material authorization (RMA) for unhealthy Juniper access points. It eliminates the manual troubleshooting checkpoints initiated by support teams, delivering significant savings in time and effort, while boosting the overall experience of users, devices, and clients.

And with an API-driven interface, Marvis can trigger these actions via webhooks and email alerts for customers to auto open support tickets or work orders in their systems.

Marvis provides proactive return material authorizations

Marvis Actions

Marvis Client

The Marvis client, currently available for Android and Windows devices, is a software agent that sits on the end-user device, collecting and displaying detailed client-device properties, including insight into client roaming behaviors.

Additionally, the Marvis Client recognizes device connection types—cellular or Wi-Fi—along with the corresponding signal strength. This additional level of granularity lets admins better understand the Wi-Fi experience directly from the client’s perspective.

The Marvis client further enriches the client device data. It goes beyond basic fingerprinting to provide an additional layer of detail, such as device type, manufacturer, and different versions of operating systems. The more details the client APK can draw out, the better the Mist AI engine gets at advanced device classification. Marvis continually learns and becomes more accurate in its ability to distinguish between device-specific issues or broad device issues, such as specifically identifying that OS version 8.1.0 is affecting certain clients.

Anomaly Detection

Marvis incorporates anomaly detection within SLEs so administrators are notified of service-impacting events and can quickly identify and resolve the root cause of issues. Anomaly detection automatically applies machine learning to establish service baselines and triggers notifications when there is a deviation from known standards. The feature uses our third generation of algorithms in long-short-term memory (LSTM) and recurrent neural networks (RNN) to boost efficacy over 95%, minimizing false positives.

Zoom Integration

With new Zoom integration capabilities, Marvis is able to collect key user experience application data for Zoom clients and correlate it with key wired, wireless, and WAN data using advanced AI/ML techniques to identify the root cause of video conferencing problems quickly.

ChatGPT Integration

Juniper has expanded Marvis's conversational interface (CI) capabilities to deliver even more human-like conversational capabilities, particularly regarding documentation and support issues.  More specifically, Marvis can now leverage a ChatGPT interface when looking for historical information from Juniper’s public-facing knowledge base information. 

Client Service Level Expectations (SLEs)

Marvis complements the client SLE framework. It applies machine learning to track and monitor client and device experiences with continuous behavioral analytics and network traffic analysis. The ability to understand these trends gives IT deeper insights for troubleshooting and planning.