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Integrating Agentic AI with Java for Autonomous Service Orchestration in Cloud-Native Systems

Paper ID:
IJSDIP_2025_VOLUME 13_ISSUE 2_36-42
Author's Name:
Dileep Kumar Ghattamaneni, Narasimha Rao Boinapalli
Volume:
13
Issue:
Issue 2
Year:
2025
Pages:
36-42

Abstract

Sustaining the development of sophisticated and autonomously intelligent orchestration techniques is a necessity due to the increasing intricacy of microservices in cloud-native systems. Classical rule-based or static approaches, such as Kubernetes YAML configurations, do not respond in real time to changes due to workload, failure, resource constraints, and other disruptions. In this document, we present an innovative framework that allows autonomous service orchestration by combining Agentic AI with microservices implemented in Java. Intelligent agents capable of goal formation, contextual reasoning, and dynamic action execution are integrated into the system, which utilizes a MAPE-K (Monitor-Analyze-Plan-Execute over Knowledge) loop to continually self-adapt. Augmentation of orchestration for Java microservices is achieved through lightweight orchestration hooks, enabling agent interaction via REST and message brokers, which facilitates coordination within the system. Compared to static, rule-based control systems, the Proposed Approach offers superior responsiveness to dynamic changes in system status, improved fault recovery performance, and enhanced resilience at the orchestration level. Additionally, the modular and policy decision framework provides explainable and extendable flexibility for the system. This work demonstrates the convergence of Agentic AI and Java services, advancing efforts to enable self-governing cloud-native infrastructures.

Keywords

Agentic AI, Autonomous Orchestration, Cloud-Native Systems, Java Microservices, Service Mesh, Intelligent Agents, Self-Adaptive Systems, Microservice

References

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