Publication Details
Abstract
Using agentic AI and real-time analytics pipelines is a disruptive solution for autonomous decision-making in real-time working data conditions. The autonomous agents that run on streaming data products and event-driven data products are explored in terms of their architecture, strategies, and practical implementation in this paper. We discuss the existing paradigms of agentic data products that have constraints in scalability, latency, and adaptability. A new layered architecture is suggested, which includes data ingestion, streaming analytics, autonomous thinking, and feedback-based adaptation. Having been built to respond to real-life problems, including constant processing of data and agent-piping, and lifecycle management, the model allows decision-making under diverse applications in a low-latency and efficient manner.
The proposed integration framework is effective as it is evaluated in a variety of use cases, such as financial risk assessment, sensor networks in the Internet of Things, and healthcare predictive systems. Such performance measures as latency, throughput, accuracy of decision making, and adaptation speed are carefully examined and found to have shown immense improvement over the traditional methods of batch-based performance. The study also proposes a code of best practices for agentic data product lifecycle management and ethical issues of autonomous systems. This work offers a solid base of scalable, adaptive, and data-driven autonomous systems through the formalization of the combination of agentic AI and real-time analytics, which can be applied to the theoretical work and its practical implementation. The work provides a solid foundation for scalable, adaptive, and technological niche of the Agentic AI within real-time settings, which has a possible high citation impact and is applicable in the context of enterprise, IoT, and financial applications.