How agentic AI can strain modern memory hierarchies

Feature Large language model inference is often stateless, with each query handled independently and no carryover from previous interactions. A request arrives, the model generates a response, and the computational state gets discarded. In such AI systems, memory grows linearly with sequence length and can become a bottleneck for long contexts. 

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 attention mechanism, across multiple stages. In an agentic workflow, the time-to-live (TTL) of ...