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

arXiv: https://arxiv.org/abs/2508.07343 · HTML: https://mlsanity.com/paper/2508.07343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 124 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 8 of 8)

- [Are Emergent Abilities of Large Language Models a Mirage?](https://mlsanity.com/paper/2304.15004/) (2023)
- [The physics of higher-order interactions in complex systems](https://mlsanity.com/paper/2110.06023/) (2021)
- [DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03882/) (2019)
- [FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems](https://mlsanity.com/paper/2201.11994/) (2022)
- [Intelligent Computing: The Latest Advances, Challenges and Future](https://mlsanity.com/paper/2211.11281/) (2022)
- [Swarm Intelligence: Past, Present and Future](https://mlsanity.com/paper/1804.07999/) (2018)
- [Massive Twinning to Enhance Emergent Intelligence](https://mlsanity.com/paper/2204.09316/) (2022)
- [Beyond the Dichotomy: How Ride-hailing Competes with and Complements Public Transport](https://mlsanity.com/paper/2104.04208/) (2021)

## Built on by (most cited first, 1 of 1)

- [Agentic Services Computing](https://mlsanity.com/paper/2509.24380/) (2025)

## Abstract (arXiv)

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.