Mist AI Management Package Boosts Enterprise Network Visibility and Data Center Control
HPE has enhanced its Mist AI management package by expanding features related to troubleshooting, visibility, and data center management within the Juniper Networking portfolio. This upgrade leverages natural language processing and AI-driven insights to help IT teams detect and resolve network issues faster and more efficiently.
AI-Powered Network Insights with Marvis Virtual Network Assistant
The Marvis AI platform collects telemetry and user state data from Juniper’s routers, switches, access points, firewalls, and applications. It then transforms this data into actionable insights and automated workflows. This approach helps IT teams quickly identify and address a wide range of network problems, improving overall enterprise network performance.
With the latest update, Marvis now uses generative AI (GenAI) to accelerate troubleshooting workflows. Operations staff can ask open-ended questions like, “Why is the Orlando site slow?” and receive intelligent, context-aware responses with clear resolutions. This upgrade shifts Marvis from simply assisting to autonomously managing network operations, simplifying tasks such as dashboard creation, cross-domain data correlation, and issue resolution through natural language commands.
Automated Issue Detection and Remediation
Marvis can now detect critical network problems including DHCP failures, missing VLANs, and network loops. It offers evidence-based recommendations to resolve these issues swiftly. Additionally, the Marvis Actions feature supports automated remediation for IT-approved scenarios, using a Human-in-the-Loop (HITL) model that builds trust over time.
Examples of automated tasks include:
- Fixing VLAN misconfigurations
- Disabling ports to stop network loops
- Upgrading devices that do not comply with standards
- Handling routine policy and firmware updates
- Resolving port-stuck problems and misconfigured access points
All actions, whether manual or autonomous, are validated after execution and logged in the Marvis Actions Dashboard. This ensures full auditability and maintains IT oversight while improving confidence in automated network management.
Extending AI Management to Data Center Networks
Juniper has integrated Marvis deeper into its Apstra data center environment by granting the AI access to Apstra’s contextual graph database. This database maps all data center components like switches, routers, servers, links, policies, and services.
This integration allows Mist AI to break down complex queries into logical parts, query multiple data sources iteratively, and generate actionable responses. Currently supporting nearly 300 API queries, the system will expand to enable autonomous service provisioning and incorporate additional data sources such as Elasticsearch. These improvements aim to support continuous learning and move closer to self-driving data centers.
Introducing Marvis Minis for Proactive Network Testing
Marvis Minis create a digital twin of the customer’s network to simulate user connections and test network configurations. This capability helps detect problems without requiring user presence or additional hardware.
In data center operations, Minis monitor vital services like DNS, network storage, and authentication. They can be activated for specific tasks such as post-maintenance validation or run autonomously at set intervals to identify issues caused by network changes or failures. This helps network operators spot difficult-to-find problems quickly.
Marvis Minis also contribute data to Juniper’s Generalized Large Experience Model (LEM), enhancing the AI’s learning and response accuracy. Originally trained on data from applications like Zoom and Microsoft Teams, the model now incorporates more generalized data for broader insight generation.
These new capabilities make the Mist AI management package a practical tool for IT managers and network teams seeking greater visibility, faster troubleshooting, and more control over enterprise and data center networks.
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