A Survey on Agentic Service Ecosystems: Measurement, Analysis, and Optimization

TL;DR

This paper reviews and proposes a comprehensive framework for measuring, analyzing, and optimizing swarm intelligence emergence in complex agentic service ecosystems, addressing current research gaps and providing practical tools.

Contribution

It introduces a unified framework with three steps—measurement, analysis, and optimization—for understanding and enhancing swarm intelligence in agentic ecosystems.

Findings

Framework clarifies cyclical mechanisms of emergence

Identifies key quantitative criteria for swarm intelligence

Analyzes strengths and limitations of existing technologies

Abstract

The Agentic Service Ecosystem consists of heterogeneous autonomous agents (e.g., intelligent machines, humans, and human-machine hybrid systems) that interact through resource exchange and service co-creation. These agents, with distinct behaviors and motivations, exhibit autonomous perception, reasoning, and action capabilities, which increase system complexity and make traditional linear analysis methods inadequate. Swarm intelligence, characterized by decentralization, self-organization, emergence, and dynamic adaptability, offers a novel theoretical lens and methodology for understanding and optimizing such ecosystems. However, current research, owing to fragmented perspectives and cross-ecosystem differences, fails to comprehensively capture the complexity of swarm-intelligence emergence in agentic contexts. The lack of a unified methodology further limits the depth and systematic treatment of the research. This paper proposes a framework for analyzing the emergence of swarm intelligence in Agentic Service Ecosystems, with three steps: measurement, analysis, and optimization, to reveal the cyclical mechanisms and quantitative criteria that foster emergence. By reviewing existing technologies, the paper analyzes their strengths and limitations, identifies unresolved challenges, and shows how this framework provides both theoretical support and actionable methods for real-world applications.