Autonomous Microservice Optimization in Cloud Environments Using Reinforcement Learning-Based AI Agents

Abstract

Purpose:
The purpose of this paper is to address the increasing complexity of managing microservices in cloud-native environments by exploring the use of autonomous, reinforcement learning (RL)-based agents for system optimization.

Design/methodology/approach
The study integrates reinforcement learning techniques into the orchestration of microservices and evaluates their impact on system performance. A layered architecture is proposed, featuring RL agents trained on real-time metrics, with experiments conducted on simulated cloud workloads to assess optimization efficiency.

Findings
The results demonstrate that RL agents significantly enhance resource allocation, reduce operational costs, and improve adherence to service-level agreements (SLAs) compared to traditional rule-based methods. Key performance indicators such as response time and resource utilization showed marked improvement.

Practical implications

This approach enables cloud platforms to operate with minimal manual tuning, offering adaptive and intelligent automation for DevOps teams. It provides a viable path toward fully autonomous cloud service management, especially for large-scale, dynamic infrastructures.

Originality/value
This paper uniquely combines historical insights with modern RL techniques to propose a novel framework for microservice optimization. It contributes to the field by demonstrating how autonomous AI agents can dynamically learn and manage cloud-native systems at scale, offering new directions for future cloud infrastructure design.