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

## Metadata
- **Title**: Complex networks of AI agentic systems: topology, memory, and update dynamics
- **Authors**: Xinyuan Song (Emory), Qingsong Wen (Oxford), Shirui Pan (Griffith), Liang Zhao (Emory)
- **DOI**: 10.36227/techrxiv.177127384.46731320/v1
- **Type**: Survey / Preprint (TechRxiv, IEEE)
- **Date**: 2026-02-16
- **License**: CC BY 4.0
- **URL**: https://www.techrxiv.org/doi/full/10.36227/techrxiv.177127384.46731320/v1

## Abstract

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.

## Key Contributions

1. **Formal definition**: Agent system as A = (V, E, M, Π) — agents, communication graph, memory configuration, policies
2. **Hierarchical taxonomy**: 3 nested dimensions → 8 system categories
3. **Communication stack**: Transport → Structural (Function Calling) → Semantic layer
4. **MCP integration**: Model Context Protocol as standardized substrate for large-scale agent networks

## Eight System Categories

| Topology | Memory | Update | Representative Systems |
|----------|--------|--------|----------------------|
| Centralized | Global | Static | MetaGPT, ChatDev, AutoGen, HuggingGPT |
| Centralized | Global | Dynamic | SWE-agent, OpenHands, Voyager, Multi-Agent Debate |
| Centralized | Local | Static | MetaAgent, YuLan-OneSim, SOTOPIA-S4 |
| Centralized | Local | Dynamic | OPTIMA, Magentic-One, G-Designer |
| Decentralized | Global | Static | BlackBoard, LLMBlackBoard, Memory Sharing |
| Decentralized | Global | Dynamic | GPTSwarm, AgentSociety, OpenAgents |
| Decentralized | Local | Static | MMAgent, WebArena, TalkHier |
| Decentralized | Local | Dynamic | Generative Agents, 1000-Person Sims, AgentNet, SOTOPIA-S |

## Key Challenges Identified
1. High communication load with agent count growth
2. Context propagation and drift under distributed execution
3. Ordering and concurrency in asynchronous systems
4. Interpretation mismatch across heterogeneous agent models
5. Update instability from concurrent state modifications
6. Security and trust as attack surface expands
