Agentic AI systems powered by LLMs are stateless by nature, with memory limited to the context window. RAG partially addresses this by enabling read-only retrieval from vector databases, but doesn't solve where agent-generated work products are stored. Agentic storage fills this gap by providing persistent, agent-aware storage. The Model Context Protocol (MCP) offers a standardized interface for agents to interact with diverse storage backends (object, block, NAS) through uniform primitives like resources and tools. Because agents can hallucinate or misinterpret instructions, agentic storage must include safety layers: immutable versioning (every write creates a new version, enabling rollback), sandboxing (constraining agent access to specific directories and operations), and intent validation (requiring agents to justify high-impact operations before execution).
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