Large language model (LLM) agents are quickly moving from “single agent” to multi-agent systems: tool-using agents, planner-orchestrator, debate teams, specialized sub-agents that collaborate to solve tasks. At the same time, the context these agents must operate within is becoming more complex: longer histories, multiple modalities, structured traces, and customized environments. This combination creates a bottleneck that looks surprisingly familiar to computer architects: memory. In...