Powering Agentic AI with AI-Ready Data Platforms That Turn Data Into Intelligence

2026-07-23 · Source: NVIDIA · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Advanced, extended

Summary

NVIDIA is redefining enterprise storage to meet the escalating demands of agentic AI, transitioning from a "system of record" to an "AI data platform" that acts as a "new data plane." This initiative, presented at GTC Taipei 2026, addresses the critical role of data in AI success, particularly with the emergence of agentic capabilities. The platform concept is structured in three layers: high-performance storage leveraging NVIDIA Vera Bluefield-4 STX for accelerated data processing; comprehensive data preparation and governance, including ingestion, cleansing, enrichment, chunking, embedding into vector databases, and indexing for AI readiness; and an optimized memory hierarchy (G1-G4, with a new G3.5 tier for KV cache via CMX) to manage agent context efficiently. NVIDIA collaborates with partners like Dell, NetApp, and IBM, providing RAG and Video Search and Summarization (VSS) blueprints to embed these real-time data processing and governance capabilities directly into storage systems at the point of data creation.

Key takeaway

For AI Architects and MLOps Engineers building agentic AI systems, your enterprise storage strategy must shift from passive record-keeping to an active "AI data platform." You should prioritize solutions that integrate real-time data preparation, governance, and context management directly at the point of data creation. This ensures agents receive accurate, timely data, mitigating risks like hallucinations and improving overall AI factory efficiency and trust. Evaluate NVIDIA's Bluefield-4 STX and partner blueprints for turnkey integration.

Key insights

Agentic AI demands a new storage paradigm: an "AI data platform" that processes and governs data in real-time at creation.

Principles

• AI success is directly proportional to data quality.
• Enterprise data now includes structured, unstructured, and agent context.
• Storage must evolve from record-keeping to context management.

Method

The proposed method involves a three-layer storage evolution: high-performance infrastructure (NVIDIA Vera Bluefield-4 STX), real-time data preparation (ingestion, cleansing, enrichment, chunking, embedding, indexing), and optimized memory hierarchy for agent context (G3.5/CMX for KV cache).

In practice

• Implement real-time data ingestion and indexing.
• Utilize vector databases for agent context.
• Integrate GPUs directly into storage platforms.

Topics

• Agentic AI
• AI Data Platforms
• Enterprise Storage
• Data Governance
• KV Cache
• NVIDIA Bluefield-4 STX
• Retrieval-Augmented Generation

Best for: CTO, VP of Engineering/Data, AI Architect, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA.