Large Language Models are fundamentally stateless functions — they map an input $x$ to an output $y$, resetting with every call. To transform these static generators into autonomous agents capable of long-horizon reasoning and lifelong learning, we must equip them with Memory.

A deep dive into the emerging intersection of graph structures and LLM agentic systems, exploring orchestration frameworks, topology-aware design, and the largely unexplored frontier of topology-based security.