Autonomous Multi-Agent System for Integrated SRE and Self-Healing in Cloud-Native Environments

DOI

10.2991/978-94-6463-950-6_35How to use a DOI?

Keywords

Site Reliability Engineering (SRE); Self-Healing Systems; Multi-Agent Systems; Autonomous Agents; AIOps; Large Language Models (LLMs); CrewAI; Automated Remediation; Incident Management

Abstract

Modern software systems, characterized by their complexity and distributed nature, pose significant challenges for Site Reliability Engineering (SRE) and DevOps teams. Traditional incident response processes are predominantly manual, reactive, and struggle to scale, leading to prolonged Mean Time to Resolution (MTTR) and increased operational overhead. This paper introduces an autonomous, self-healing framework designed to address these limitations. The proposed system leverages a multi-agent architecture, built using CrewAI and powered by advanced Large Language Models (LLMs) like DeepSeek-R1 and GPT-4o, to automate the entire incident management lifecycle. By integrating seamlessly with standard DevOps toolchains, including Kubernetes for orchestration, Middleware.io for monitoring, Jira for incident tracking, and GitHub for version control, the framework moves beyond simple alert triaging to proactive, automated remediation. The system demonstrated exceptional performance in a simulated production environment, achieving up to 96% accuracy in root cause analysis (RCA), a 73% success rate in automated code fix generation, and reducing the average end-to-end resolution time from hours to under 28 min. This work establishes an auditable, human-in-the-loop solution that significantly reduces manual effort, minimizes system downtime, and enhances the resilience of modern software operations.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. V. Manohar
AU  - Ravi Shukla
AU  - Shinu Abi
PY  - 2025
DA  - 2025/12/29
TI  - Autonomous Multi-Agent System for Integrated SRE and Self-Healing in Cloud-Native Environments
BT  - Proceedings of the International Conference on Smart Systems and Social Management (ICSSSM 2025)
PB  - Atlantis Press
SP  - 525
EP  - 535
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-950-6_35
DO  - 10.2991/978-94-6463-950-6_35
ID  - Manohar2025
ER  -