Agentic AI and Its Applications in Data Analytics

Abstract Traditional data analytics pipelines rely heavily on static dashboards, predefined queries, and continuous human supervision, which limits their ability to react to fast-changing data and business conditions. This paper examines agentic AI, a class of autonomous, goal-driven, and tool-using artificial intelligence systems built on large language models (LLMs), and its emerging role in reshaping data analytics workflows. Unlike conventional generative AI, which produces a single response to a prompt, agentic systems perceive an environment, plan multi-step actions, invoke external tools such as databases and code execution environments, and reflect on outcomes before acting again. This study surveys the architectural foundations of agentic AI, including perception-planning-action loops, memory mechanisms, and multi-agent orchestration, and analyzes their application across automated data cleaning, natural-language-to-insight querying, autonomous anomaly detection, predictive and prescriptive analytics, and collaborative multi-agent reporting. Comparative analysis of prominent orchestration frameworks and illustrative industry case studies in finance and retail analytics is presented alongside a discussion of open challenges, including hallucination risk, governance, and evaluation difficulty. The findings suggest that agentic AI can substantially reduce human-in-the-loop bottlenecks in analytics while introducing new requirements for oversight and explainability. Keywords: Agentic AI, Autonomous Agents, Data Analytics, LLM Agents, Multi-Agent Systems, Business Intelligence, Automated Insights, Reasoning and Planning, Tool Use, Retrieval-Augmented Generation