SNIA Developer Conference September 15-17, 2025 | Santa Clara, CA
The rise of Generative and Agentic AI has driven a fundamental shift in storage —from storing data to functioning as comprehensive knowledge management systems. Traditional model of storing data and system metadata and providing analytical capabilities on top of it is now inadequate. Agentic AI workflows require access to semantically enriched representations of data, including embeddings and derived metadata (e.g., classification, categorization). As data is ingested, storage systems must support real-time or near-real-time generation and association of such metadata. Industry’s initial response involved deploying separate document and embedding stores alongside conventional storage. However, the disaggregation of data and its semantic representations across multiple systems introduces significant challenges—including maintaining consistency, enforcing security policies, and ensuring low-latency access. To address these constraints, computation, storage and access of enriched data must be co-located with the primary data . This has driven the evolution of storage into unified knowledge platforms that natively compute, persist, and index vectors and derived metadata with underlying data and system metadata. This rearchitecting affects not just data, but also how storage systems are administered. Protocols like the Model Context Protocol have been introduced to facilitate interaction and administration of system and data. Solutions like HPE Alletra Storage MP X10000 exemplify this evolution, offering integrated capabilities to support AI-native workloads. As these platforms mature, there is a need to standardize access to knowledge and semantic representations for seamless integration with application . This talk explores the evolution of storage platforms and the emerging capabilities required to support this new paradigm.
The rise of Generative and Agentic AI has driven a fundamental shift in storage —from storing data to functioning as comprehensive knowledge management systems. Traditional model of storing data and system metadata and providing analytical capabilities on top of it is now inadequate.
