Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data, execute tools, and generate new results, storage systems must continuously supply and preserve the data that moves the agent reasoning loop.
Each agent step can trigger multiple storage operations, and those operations can repeat across thousands of concurrent agents with increasingly larger context windows. Supplying and preserving this data requires more than basic reads and writes. AI inference runs on GPUs, but agentic processes, tool calls, data management tasks, and the storage services that support them run on CPUs.
During writes, storage may compress and encrypt data, calculate checksums, and calculate redundancy. During reads, it may validate, decrypt, decompress, or reconstruct data before returning it to the application. These functions are essential to the security and resilience of AI systems. Each function also requires additional CPU processing as data moves through the storage path.
As agent concurrency (multiple users, AI agents, or tasks running in parallel) and context volumes grow, storage must perform more of this work without constraining application responsiveness or token generation; it must supply data at the rate required for accelerated computing.
