Autonomic Microservice Management via Agentic AI and MAPE-K Integration

2025-06-27

AI Summary

The decentralized nature of microservice architectures exacerbates security risks and operational complexity, undermining system stability and resilience. To address this, we propose an autonomous management framework that synergistically integrates Agentic AI with the MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) closed-loop control model—marking the first approach to enable multi-agent collaborative dynamic anomaly detection, root-cause inference, and self-healing response. The framework unifies real-time monitoring, online learning, policy-driven execution, and feedback-based optimization, supporting configurable, environment-aware policy customization for heterogeneous microservice deployments. Experimental evaluation demonstrates significant reduction in mean time to recovery (MTTR), alongside measurable improvements in security posture, system elasticity, and quality of service (QoS). Our solution provides a production-ready, autonomous governance mechanism for industrial-scale microservice systems.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Hardware-aware ML

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

While microservices are revolutionizing cloud computing by offering unparalleled scalability and independent deployment, their decentralized nature poses significant security and management challenges that can threaten system stability. We propose a framework based on MAPE-K, which leverages agentic AI, for autonomous anomaly detection and remediation to address the daunting task of highly distributed system management. Our framework offers practical, industry-ready solutions for maintaining robust and secure microservices. Practitioners and researchers can customize the framework to enhance system stability, reduce downtime, and monitor broader system quality attributes such as system performance level, resilience, security, and anomaly management, among others.

Problem

• Autonomous anomaly detection in microservices using agentic AI
• Decentralized microservice management challenges addressed via MAPE-K
• Enhancing system stability and security in distributed cloud environments

Innovation

• Agentic AI for autonomous anomaly detection
• MAPE-K framework for microservice management
• Customizable solutions for system stability