Complex networks of AI agentic systems: topology, memory, and update dynamics

Large-scale networks of agents are increasingly applied to software engineering, scientific analysis, web automation, organizational workflows, and social simulation, yet existing multi-agent architectures lack a unified framework to explain why some designs scale to long-horizon, multi-step tasks while others fail. As these systems grow, their behavior is fundamentally shaped by how agents are connected, how information is stored, and how states are updated over time. In this survey, we introduce a hierarchical taxonomy of agent systems along three core dimensions-architecture topology (centralized vs. decentralized), memory scope (global vs. local), and update behavior (static vs. dynamic)-which together induce eight system categories that organize prior work and make architectural trade-offs explicit. Using this taxonomy, we analyze how design choices influence scalability, coordination efficiency, communication overhead, planning depth, and robustness under partial failure, and we identify common failure modes and open challenges, including consistency management, agent routing, federation boundaries, and stability under noise or disruption.