Mapping Large-Scale AI Agent Networks: A 3‑Dimensional Classification Framework

Growth of Agent Marketplaces and Systems

Recent years have seen rapid expansion of both AI agent marketplaces and deployed agent systems. The number of available agents and their categories are increasing, while real‑world deployments have evolved from a few cooperating roles to structures involving dozens or hundreds of agents. This shift moves large‑scale agent networks from laboratory demos to open, continuous environments.

Three‑Dimensional Classification Framework

A recent review proposes a unified three‑dimensional framework to describe large‑scale agent networks. The dimensions are:

Topology : centralized vs. decentralized architectures.

Memory scope : global memory vs. local memory.

Update behavior : static vs. dynamic operation.

Combining the three binary choices yields eight typical categories of large‑scale agent networks.

Implications of the Three Axes

Different combinations affect coordination efficiency, scalability, robustness, and long‑term dynamics:

Centralized systems simplify scheduling and consistency but risk a central bottleneck as the network grows.

Decentralized systems enable emergent behavior and flexibility but are prone to local miscoordination and information drift.

Global memory supports shared context and state alignment, while local memory mirrors realistic distributed settings but can cause divergent views.

Static update eases analysis and reproducibility; dynamic update better fits long‑horizon tasks and adaptive collaboration.

World‑Model Consistency as the Primary Bottleneck

The review emphasizes that the deepest limitation is not communication protocols but inconsistencies in agents' internal world models. Even with perfect message transmission, differing knowledge, preferences, or memories lead to belief drift, unstable cooperation, goal divergence, and non‑stationary system dynamics.

Research Directions

Based on these observations, the authors recommend focusing on:

Clearer consistency models that define how agents align their world representations.

Stronger shared‑state control mechanisms to enforce coherent updates.

Advanced routing and scheduling strategies for both centralized and decentralized topologies.

Robust identity, security, and resilience designs for open, large‑scale environments.

Evaluation benchmarks that scale beyond small‑scale setups to thousands or millions of agents.

Paper link: https://www.techrxiv.org/doi/full/10.36227/techrxiv.177127384.46731320/v1

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来源：ScienceAI
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