Mist AI management package gains enhanced enterprise visibility and data center control features.
HPE has broadened the scope of its recently acquired Juniper Networking portfolio with expanded troubleshooting, visibility and data center features in its Mist AI management package.
The natural-language Mist AI and its Marvis virtual network assistant (VNA) platform work by gathering telemetry and user state data from Juniper’s routers, switches, access points, firewalls, and applications to offer actionable insights and automated workflows to detect and resolve a broad range of enterprise networking problems.
“The Marvis AI engine is continuously learning from relevant telemetry across networking domains and external applications—improving the efficacy of AI results, speeding up issue resolution, and assuring end user experiences from client to cloud,” wrote Jeff Aaron, vice president of networking product and solution marketing, HPE in a blog post about the upgrades.
With this update, the Marvis Conversational Interface can now use GenAI to accelerate troubleshooting by understanding agentic AI workflows.
“Operations can now communicate with the network, asking open-ended questions such as, ‘Why is the Orlando site slow?’, and get intelligent analysis, contextual understanding, and precise resolutions in response. This evolution in Marvis AI Assistant marks a shift from assisted operations to autonomous networking intelligence, simplifying tasks like dashboard generation, cross-domain data correlation, and issue resolution through natural language input,” Aaron stated.
In addition, Marvis can now identify critical issues such as DHCP failures, missing VLANs, and network loops and provide evidence-based recommendations for rapid remediation, Aaron stated.
Further, Aaron stated that Marvis Actions offers automated remediations for IT-approved scenarios. Using a Human-in-the-Loop (HITL) trust model, customers can develop confidence over time, giving Marvis AI Assistant permission to automatically resolve problems such as:
- Correcting VLAN misconfigurations
- Shutting down ports to resolve network loops
- Upgrading noncompliant devices
- Handling routine policy updates and firmware compliance
- Resolving port-stuck issues and misconfigured access points
“Each action, whether initiated by IT or executed autonomously by Marvis AI Assistant, is validated post-remediation and logged in the Marvis Actions Dashboard. This maintains full auditability and HITL oversight while building trust through consistent, accurate results,” Aaron wrote.
Juniper also extended Marvis further into the vendor’s Apstra data center networking environment by letting the platform have access to Apstra’s contextual graph database, which maps the components in the data center including switches, routers, servers, links, policies and services.
The idea is to let the MistAI framework understand complex queries, break them into logical components, and iteratively query data sources to synthesize actionable responses, Aaron stated.
“This framework currently supports nearly 300 API queries. It will expand to enable autonomous service provisioning activities, incorporate additional data sources like elastic search, and enhance feedback mechanisms for continuous learning—critical steps toward fully self-driving data centers,” Aaron wrote.
In addition to the Apstra extension, Juniper is adding Marvis Minis capabilities to data center operations. Marvis Minis set up a digital twin of a customer’s network environment to simulate and test user connections, validate network configurations, and find/detect problems without users being present and without requiring any additional hardware, according to Juniper.
In the data center, Minis can now watch over core functions such as DNS, network storage, and authentication services.
“Network operators can activate Minis for specific scenarios like post-maintenance validation or run them autonomously with configurable intervals to detect issues caused by network changes or failures, allowing customers to find ‘needle in a haystack’ issues quickly,” Aaron wrote.
Minis can now feed additional information into HPE Junipers’ networking Generalized Large Experience Model (LEM) to further enhance Marvis’s learning and responses. Originally trained on data input from data gleaned from Zoom and Microsoft Teams applications, now can gather more generalized training data to broaden insights, Aaron stated.
