# Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems

arXiv: https://arxiv.org/abs/2508.20508 · HTML: https://mlsanity.com/paper/2508.20508/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2025.

## Built on by (most cited first, 8 of 8)

- [Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models](https://mlsanity.com/paper/2510.01231/) (2025)
- [Modular Task Decomposition and Dynamic Collaboration in Multi-Agent Systems Driven by Large Language Models](https://mlsanity.com/paper/2511.01149/) (2025)
- [Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning](https://mlsanity.com/paper/2511.03279/) (2025)
- [Knowledge-Augmented Large Language Model Agents for Explainable Financial Decision-Making](https://mlsanity.com/paper/2512.09440/) (2025)
- [Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders](https://mlsanity.com/paper/2511.00462/) (2025)
- [Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks](https://mlsanity.com/paper/2511.12122/) (2025)
- [Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphs](https://mlsanity.com/paper/2512.18673/) (2025)
- [Shared Representation Learning for High-Dimensional Multi-Task Forecasting under Resource Contention in Cloud-Native Backends](https://mlsanity.com/paper/2512.21102/) (2025)

## Abstract (arXiv)

This paper proposes an intelligent service optimization method based on a multi-agent collaborative evolution mechanism to address governance challenges in large-scale microservice architectures. These challenges include complex service dependencies, dynamic topology structures, and fluctuating workloads. The method models each service as an agent and introduces graph representation learning to construct a service dependency graph. This enables agents to perceive and embed structural changes within the system. Each agent learns its policy based on a Markov Decision Process. A centralized training and decentralized execution framework is used to integrate local autonomy with global coordination. To enhance overall system performance and adaptability, a game-driven policy optimization mechanism is designed. Through a selection-mutation process, agent strategy distributions are dynamically adjusted. This supports adaptive collaboration and behavioral evolution among services. Under this mechanism, the system can quickly respond and achieve stable policy convergence when facing scenarios such as sudden workload spikes, topology reconfigurations, or resource conflicts. To evaluate the effectiveness of the proposed method, experiments are conducted on a representative microservice simulation platform. Comparative analyses are performed against several advanced approaches, focusing on coordination efficiency, adaptability, and policy convergence performance. Experimental results show that the proposed method outperforms others in several key metrics. It significantly improves governance efficiency and operational stability in large-scale microservice systems. The method demonstrates strong practical value and engineering feasibility.