Agentic AI requests trigger numerous model calls, tool calls, memory lookups, policy checks, storage accesses, and network transfers before generating a final answer. As more agents run concurrently and maintain context across sessions, the underlying infrastructure must rapidly move, protect, retrieve, and reuse data.
Agentic AI shifts compute demand from single-model inference to complex, multi-step data orchestration, increasing pressure on memory, storage, and networking I/O.