A Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making in Software Pipelines
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P128Keywords:
Multi-Agent Systems, DevOps Automation, CI/CD Pipelines, Artificial Intelligence, Reinforcement Learning, Cloud Computing, Distributed Systems, Self-Healing Systems, Microservices, Intelligent OrchestrationAbstract
The software development environment is changing quickly into highly distributed, cloud-native and microservices-based architectures. As these types of environments grow, orchestrating, monitoring, scaling, failure recovery, and assuring the integrity of delivery of DevOps pipelines becomes more complex. Traditional DevOps automation tools mainly use static CI/CD pipelines and rule-driven automation scripts that can be inadequate for large-scale, dynamic and heterogeneous deployment environments. In this paper a Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making (MAF-DDACD) is proposed to provide the software delivery pipelines with greater autonomy, intelligence, and resilience. The proposed framework uses multiple specialized AI agents, one for each of the following DevOps tasks: build automation, test orchestration, security scanning, deployment optimization, infrastructure scaling, and anomaly detection. They work together via a shared knowledge layer, and a consensus-based decision engine to get the best result from the pipeline in uncertain and dynamic environments. To enhance adaptability and inter-agent coordination, reinforcement learning (RL), natural language processing (NLP) and graph-based dependency modeling are integrated. It also adds a hierarchical orchestration layer which dynamically assigns tasks to agents based on workload, system health and historical performance metrics. Unlike traditional DevOps systems, MAF-DDACD enables self-healing pipelines, failure prediction, and failure avoidance rollback strategies. The results of the experimental simulation show that the proposed system can be beneficial for deployment efficiency, reduce failure ratio of pipelines, and improve mean-time-to-recovery (MTTR). It delivers up to 32% lower deployment latency, 27% higher pipeline success rate, and 41% quicker response time for detecting anomalies, over traditional CI/CD pipelines. Moreover, the decision-making collaborative mechanism allows agents to reach a consensus on resolving conflicts in a weighted voting and confidence scoring model, which ensures both reliable and explainable automation decisions. The framework can be scaled to hybrid cloud deployments and is open to be integrated with other DevOps tools like kubernetes, jenkins, and terraform. The study concludes that multi-agent AI systems offer a promising direction for future DevOps automation to support intelligent, adaptive, and resilient pipelines for software engineering and support for future scale distributed systems.
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