NetApp hands storage ops to AI agents with NetApp Console

The setup Singh describes is familiar to anyone running enterprise storage: data now sits across on-premises data centers, public clouds and neoclouds, and customers will keep choosing a mix of locations. His answer is not another silo but one data foundation that behaves the same way everywhere, so capabilities and operational experience stay consistent across environments. Yu frames the same problem as organizational rather than technical, noting that teams without consistent, trusted data stop making decisions and start building workarounds — the ground where shadow AI and shadow IT take hold.

NetApp's response pairs that consistent data plane with a unified control plane shared by agents and humans. The boundary is the point: customers set policies and boundaries across their fleet, and agents operate inside them. Singh's example of agents catching a nighttime anomaly, applying quality of service rules in real time and leaving an audit trail is the clearest picture of what that division of labor looks like in practice.

What the approach appears to hinge on is accountability rather than raw automation. Yu's argument — that letting an algorithm decide is not a governance model, and that agents belong on a RACI chart with clear owners for data and access — suggests the value of autonomous storage operations may depend less on how much the agents can do than on whether organizations can say who is responsible when they act.