Intelligent Enterprise Automation Using Agentic AI and Predictive Infrastructure Analytics
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P122Keywords:
Agentic AI, Enterprise Automation, Predictive Infrastructure Analytics, Autonomous Systems, Intelligent Orchestration, Machine Learning, Infrastructure Observability, Generative AI, Predictive Maintenance, Digital TransformationAbstract
The rapid evolution of enterprise digital transformation has accelerated the adoption of intelligent automation technologies across modern organizations. Traditional automation systems, which rely heavily on predefined workflows and static rule engines, often struggle to address dynamic business environments characterized by large-scale data generation, infrastructure complexity, and real-time decision-making requirements. In this context, Agentic Artificial Intelligence (AI) and Predictive Infrastructure Analytics have emerged as transformative paradigms capable of enabling autonomous enterprise operations, adaptive orchestration, and predictive decision intelligence. This research article explores the integration of Agentic AI with predictive infrastructure analytics to establish intelligent enterprise automation frameworks capable of self-learning, autonomous coordination, and proactive operational optimization. The study examines how autonomous AI agents interact with enterprise systems to monitor infrastructure behavior, detect anomalies, predict failures, and optimize business processes without extensive human intervention. The research further analyzes the role of machine learning, reinforcement learning, generative AI, and predictive analytics in enhancing infrastructure resilience, operational efficiency, and business continuity. Comparative evaluations between traditional automation systems and AI-driven intelligent enterprise frameworks are presented to demonstrate the advantages of adaptive automation models. The proposed framework emphasizes intelligent orchestration, predictive maintenance, infrastructure observability, and autonomous remediation mechanisms. The findings indicate that enterprises implementing Agentic AI-based automation architectures achieve significant improvements in operational scalability, predictive accuracy, infrastructure reliability, resource optimization, and incident response efficiency. The article also identifies major implementation challenges including data privacy, explainability, governance complexity, and computational overhead. The study concludes that intelligent enterprise automation represents a foundational pillar for next-generation digital enterprises and highlights future research directions involving cognitive orchestration, multi-agent collaboration, and self-healing infrastructures.
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