Dell PowerScale: The Data Foundation for Agentic Retrieval

• Agentic retrieval shifts AI from one-time indexing to continuous exploration of live enterprise data.
• That change raises the bar for storage: metadata performance, permission-aware access, and in-place governance become part of the retrieval experience.
• The faster agents get, the more the bottleneck moves from GPUs to the data layer feeding them.
• PowerScale is built for this access pattern: fast metadata at namespace scale, multi-protocol access with zero data movement, and security inherited from your existing model.

The first wave of enterprise AI was built on static RAG pipelines: chunk your documents once, embed them into a vector database, and hope the right snippet surfaces at query time. That approach is giving way to something more powerful — agentic retrieval, where AI agents actively explore your data the way a skilled analyst would. They search by keyword and metadata, follow references across documents, open full files, run queries and iterate until they find the answer. This shift changes what AI demands from storage. Instead of a one-time embedding pass, agents generate continuous, concurrent, fine-grained access to live enterprise data: heavy metadata operations, random reads across enormous namespaces and repeated traversal of the same datasets by many agents at once. The storage platform is no longer behind the AI pipeline — it is the retrieval surface. Step back, and the pattern is clear: the bottleneck to AI velocity is increasingly data, not compute. When agents spend their time finding, reading and traversing enterprise data, storage stops being plumbing behind the model and becomes part of the AI system itself. That reality is what the Dell AI Data Platform is built around — Dell’s modular approach to making enterprise data AI-ready. Rather than one monolithic system, it pairs data engines that discover, process and search information with storage engines that hold and serve it at scale. For agentic workloads, the storage engine layer is decisive: it is where live enterprise data sits, and where every retrieval ultimately lands. For unstructured data — the documents, files, and shares agents spend most of their time in — that storage engine is PowerScale. It is the foundation agentic retrieval runs on: the layer that keeps enterprise data live, governed, and directly usable by AI, in place and without a separate silo to manage.

Why PowerScale for agentic retrieval

Seven properties make it the right foundation for agentic retrieval:

• One namespace, every protocol, zero data movement. Agents need to read data where it lives. OneFS presents a single scale-out namespace — up to multiple petabytes — accessible simultaneously over NFS, SMB, S3 and HDFS. The contracts your legal team saves over SMB are instantly readable by an agent over NFS or S3, with no copies, no sync jobs and no AI data silo to govern separately.
• Metadata performance at namespace scale. Agentic workloads are metadata-intensive: directory walks, attribute lookups, change detection, and targeted searches across billions of files. PowerScale’s distributed metadata architecture and all-flash node options keep these operations fast, while MetadataIQ streams filesystem metadata to an external index — giving agents and ingestion pipelines an instantly queryable map of the entire namespace without crawling it.
• Always-current knowledge with the PowerScale RAG Connector. For hybrid architectures that still maintain vector indexes, Dell’s open-source RAG Connector uses MetadataIQ to identify exactly which files changed since the last ingestion pass. Pipelines process only the delta — keeping retrieval fresh while cutting ingestion compute. It integrates natively with LangChain and NVIDIA NeMo Retriever / NIM microservices.
• Permission-aware by design. An agent should never surface a document its user couldn’t open themselves. Because agents read directly from OneFS, every access is evaluated against the same ACLs, identities and access zones that govern your users today — and revoking access takes effect immediately, on the next read. Retrieval inherits your existing security model instead of bypassing it — a critical gap in copy-out architectures, where embedded content silently escapes its source permissions.
• Visible, encrypted, and accountable. Agents are a new class of data consumer, and OneFS treats them like any other: protocol auditing records every AI access in the same stream your security tools already consume, so “what did the agent read?” always has an answer. Data-at-rest encryption and in-flight protocol encryption keep content protected end to end, and role-based administration keeps the AI platform team’s reach scoped to exactly what they operate.
• Validated for the AI factory. PowerScale is certified Ethernet-based storage for NVIDIA DGX SuperPOD, validated in the NVIDIA Cloud Partner storage program, and NVIDIA-Certified Enterprise Storage — with GPUDirect Storage and NFS over RDMA for the highest-throughput stages of your pipeline. The same platform that feeds training and fine-tuning serves agentic inference, as a core storage option within the Dell AI Factory with NVIDIA.
• Cloud-native ready. Agentic stacks run on Kubernetes. The Dell CSI driver for PowerScale provisions persistent storage for vector databases, model caches, and agent workspaces — with quotas, snapshots, and zone-aware isolation for multi-tenant platforms.

What agentic retrieval looks like in practice

Individually these are capabilities; together they’re what lets an agent work against live enterprise data — fast, current and inside your existing security model. Across the enterprise, that’s already taking concrete shape

• Enterprise knowledge agent. A support or operations copilot answers questions from decades of accumulated content — runbooks, tickets, contracts, project shares — that already lives on PowerScale via SMB. Agents search the namespace through MetadataIQ’s index, open only the relevant documents over NFS or S3, and answer with citations. Because access flows through OneFS permissions, the finance agent can’t read HR’s share, and neither can the employee asking it questions.
• Engineering and design intelligence. Agents traverse CAD vaults, simulation outputs, firmware repositories, and test logs to answer “have we seen this failure mode before?” — workloads dominated by metadata lookups and targeted random reads across millions of small files, the access pattern OneFS scale-out architecture was designed for.
• Regulated document analysis. Banks and insurers point agents at contract repositories and filings for clause extraction, exposure analysis and audit response. WORM (SmartLock) retention, encryption at rest and full audit logging mean the retrieval layer satisfies the same compliance regime as the data itself.
• Research and life sciences. Agents correlate instrument output, imaging metadata, and publications across petabyte-scale project directories — combining high-throughput streaming reads for large files with index-driven discovery across the namespace.
• Multi-tenant AI services. Service providers offer “agents over your data” as a product. Access zones give each tenant an isolated namespace, identity store, and network presence on shared infrastructure; SmartQuotas and dedicated IP pools keep tenants metered and separated, while the provider operates one platform.

Different as they look, these workloads make one demand of storage: fast, concurrent, governed access to live data — at machine speed, against your most sensitive content.

The bottom line

Retrieval architectures will keep evolving — today’s agentic patterns will blend with whatever comes next. The requirement beneath them won’t: fast, governed, in-place access to enterprise data at scale. That’s the layer PowerScale provides. See PowerScale in action as the data foundation for agentic retrieval. Explore the platform or talk to your Dell account team about an agentic retrieval architecture review for your environment.