Hewlett Packard Enterprise’s self-driving network can detect and resolve 70-80% of potential network issues without a human operator, networking chief Rami Rahim told Fierce Network. The system operates autonomously on poor performance, slow connections, roaming failures, and DNS problems that keep clients from connecting before notifying IT staff about the fix only after it is made.
How Marvis AI Handles Network Troubleshooting
The 70-80% resolution figure comes from HPE logging each autonomous fix. Across cloud platforms like HPE Mist and HPE Aruba Central, incoming customer service tickets are initially processed by the Marvis AI engine, which either fixes the issue immediately or acquires the knowledge for future automated resolutions.
Adoption relies heavily on a gradual, trust-building approach where customers authorize automated actions individually, one capability at a time. In the past 90 days, approximately 10,000 self-driving actions were approved by customers across HPE’s cloud platforms, as noted by Sujai Hajela, EVP and GM of HPE’s campus and branch business.
Where Human Operators Remain Essential
Despite autonomous capabilities, networks still require human control when responding to active attackers or implementing changes involving regulatory compliance. Security incidents with an active attacker are beyond the system today, though Rahim noted the AI will grow into pushing policy across the whole network, not only to firewalls, over time.
Because regulated industries must secure explicit customer consent and maintain the ability to restore the previous network state—while proving the new setup is superior—changes tied to compliance standards take longer to transition to automated self-driving modes than standard workplaces.
Enterprise Results and Rising Non-Human Traffic
Autonomous capabilities have become increasingly urgent as non-human data consumption outpaces human users. Cloudflare data from May indicates that bot traffic has surpassed human traffic and has reached nearly two-thirds of internet traffic. Rahim emphasized that because non-human traffic doesn’t necessarily have the same ability to complain, continuous measurement of user and device experience, rather than simple up-or-down monitoring, is a requirement, along with self-driving operations.
Carrier Strategies and Infrastructure Expansion
Telecommunications operators are monetizing AI traffic by cutting operating costs with agentic AI operations, selling high-bandwidth, low-latency data center interconnect (DCI) fabrics, and using central offices near customers to offer AI inference. Telcos’ earliest AI inference businesses are sovereign GPU-as-a-service offerings sold mostly outside the U.S.
Meanwhile, HPE is expanding its hardware footprint through major supply agreements. On the morning of Sept. 30, coinciding with investor day, the company secured its initial Helios order through a $1.2 billion agreement with Vultr. Rahim said Helios represents a networking opportunity of more than $1 billion over two years. HPE also cited a potential multiyear agreement, announced Sept. 2, under which Oracle will deploy HPE Juniper Networking routers and switches across its AI data centers.
Frequently Asked Questions
What specific network problems can HPE’s AI resolve autonomously?
Autonomous network mitigation covers challenges such as roaming failures, slow connections, substandard performance, and DNS errors preventing client connectivity.
How many self-driving actions did customers authorize recently?
Over the last 90 days, clients authorized roughly 10,000 self-driving tasks throughout HPE cloud platforms, according to Sujai Hajela.
What role did AMD Helios play in HPE’s recent financial forecasts?
HPE landed its first Helios order in a $1.2 billion deal with Vultr, announced Sept. 30. Rahim said Helios represents a networking opportunity of more than $1 billion over two years.
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