NetApp Integrates AI Agents for Autonomous Storage Under Human Supervision
NetApp is introducing AI agents to manage hybrid cloud storage operations autonomously, relying on human-defined policy guardrails and the RACI framework to maintain governance.
As artificial intelligence continues to reshape enterprise IT, virtually every workload is becoming a data workload. This shift is turning data governance into a critical test of whether organizations can safely trust autonomous systems to manage their underlying infrastructure. The core challenge is no longer just deciding where data should reside, but determining which autonomous systems are permitted to interact with it.
Maintaining Consistency Across Hybrid Environments
Today's enterprise data is highly distributed, spanning on-premises data centers, public clouds, and specialized neoclouds. Sandeep Singh, senior vice president and general manager of enterprise storage at NetApp Inc., emphasizes that adding more data silos to accommodate AI is the wrong approach. Instead, Singh advocates for a singular data foundation that delivers consistent behavior regardless of where the data is stored.
According to Singh, organizations need a unified data infrastructure strategy to ensure they receive a consistent set of capabilities and operational experiences across diverse environments. Without this consistency, technical and organizational friction quickly arises. Helen Yu, founder and chief executive officer of Tigon Advisory Corp., notes that when teams do not have reliable access to consistent data, they often stop making decisions or create workarounds. This breakdown in trust is what drives the adoption of shadow AI and shadow IT, making it a leadership and accountability problem rather than a simple storage issue.
Autonomous Infrastructure Management with Guardrails
To address these challenges, NetApp has paired its data plane with a unified control plane accessible by both human operators and AI agents. Delivered via the NetApp Console, these hybrid cloud tools permit administrators to establish high-level policies and boundaries across their entire infrastructure. Within these predefined guardrails, AI agents are given the autonomy to manage day-to-day operations.
During a demonstration at the NetApp INSIGHT event, Singh illustrated how this works in practice. An AI agent detected a nighttime performance anomaly that would typically trigger an alert for an engineer. Instead of waking up a human worker, the agent automatically applied quality-of-service rules to resolve the issue and eliminate the "noisy neighbor" effect. The agent resolved the problem within the established boundaries and left a detailed audit trail for human review the following morning.
However, Yu cautions that letting an algorithm make decisions does not replace a true governance model. She suggests that enterprises should evaluate automation based on business outcomes rather than simply counting the number of tasks removed. To maintain clear lines of authority, Yu recommends incorporating AI agents directly into organizational RACI (responsible, accountable, consulted, and informed) charts to define who owns the data, who can access it, and who holds the authority to override an automated exception.
What it means for developers
For developers, the shift toward agentic infrastructure means less time spent managing routine operations and more time focused on building applications. As AI agents take over low-level tasks like performance optimization and anomaly detection, developers can rely on a more stable, self-healing data layer. However, this transition also requires developers to design systems with strict policy guardrails and comprehensive audit trails in mind.
To build and test the next generation of autonomous applications, developers need access to the best underlying technology. Through Apixo, developers can easily experiment with top AI models cheaply using a single API key at https://apixoai.online. This streamlined access helps teams prototype agentic workflows without the complexity of managing multiple API integrations or facing prohibitive costs.
Ultimately, as platforms like NetApp prove that AI agents can safely manage enterprise infrastructure, developers will be tasked with writing the rules that govern these machines. Success in this new landscape will depend on a developer's ability to integrate robust governance frameworks, like RACI, directly into their software architecture, ensuring that autonomous systems always operate within human-defined boundaries.
Source: NetApp hands storage operations to AI agents, but humans still draw the boundaries — SiliconANGLE AI. Written by the Apixo team from that report.
One key for Claude, GPT, GLM, DeepSeek and more. Pay per token with crypto.
Get your API key
