"Agentic AI refers to systems that maintain continuity across many steps. These AI agents don't answer a single question before resetting. They engage in extended workflows, remembering past instructions and building on intermediate results over time. In these multi-turn scenarios, the conversation context becomes a critical, persistent state rather than a transient input. This creates a memory residency requirement. The inference engine cannot simply discard the state after generating a token. It must maintain the Key-Value (KV) cache, which is the intermediate representation of the"
"So even though agentic algorithms might orchestrate multiple reasoning paths at the software level, the underlying inference process remains deterministic. Managing this branching and extended KV cache across multiple steps therefore requires memory capable of rapid switching between different context states. In effect, memory becomes a record of the agent's reasoning process, where any prior node may be recalled to inform future decisions. As a result, the emergence of agentic AI systems is shifting the bottleneck from raw compute to memory capacity, bandwidth, and hierarchical design."
Large language model inference is often stateless, with each query handled independently and computational state discarded after response generation. Agentic AI maintains continuity across many steps, engaging in extended workflows that remember past instructions and build on intermediate results. Agentic workflows convert conversation context into a persistent, critical state rather than a transient input, creating a memory residency requirement. Inference engines must retain the Key-Value (KV) cache across stages, extending inference context TTL to minutes, hours, or days in asynchronous workflows. This shifts the bottleneck from raw compute to memory capacity, bandwidth, and hierarchical design, revealing limits of existing hierarchies from HBM to network-attached storage.
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