Beyond Traditional AI: A Comprehensive Study of Agentic Frameworks and Their Impact

Abstract

The Agentic AI framework introduces a new paradigm for artificial intelligence, which allows autonomous decision-making, dynamic task execution, and multi-agent collaboration. This research examines the fundamental components of agentic AI, including tool use, reflection, planning, and swarm intelligence, by analyzing the LangGraph, CrewAI, and OpenAI Swarm architectures. The research evaluates agentic AI through its proactive nature, looped reasoning capabilities, and its ability to adapt in complex environments, comparing it to traditional systems. The transformative power of agentic AI becomes evident through its applications in healthcare, finance, supply chain management, and scientific discovery, which boost operational efficiency and innovation. The responsible deployment of agentic AI necessitates solutions to ethical design issues, regulatory compliance problems, and challenges related to bias mitigation and interoperability. Research should focus on developing adaptive reskilling methods, transparent accountability systems, and energy-efficient models for the future. The combination of interdisciplinary knowledge with strong governance systems enables agentic AI to advance technology sustainably while maintaining ethical standards.