A COMPARATIVE STUDY OF PREDICTIVE AI AND AGENTIC AI: EXAMINING KEY DIFFERENCES IN AUTONOMY, GOAL ORIENTATION, HUMAN INTERACTION, AND SYSTEM ACCOUNTABILITY

This study conducts a comprehensive comparison between predictive AI and agentic AI, focusing on critical dimensions such as autonomy, goal orientation, human interaction, and system accountability. Employing a mixed-methods approach, the research analyzes datasets from industry reports, scholarly literature, and hypothetical case studies to highlight distinctions. Key findings reveal that predictive AI excels in data-driven forecasting with limited autonomy, relying heavily on human oversight for decision-making, while agentic AI demonstrates higher independence in pursuing complex goals, adapting dynamically to environments. The analysis underscores agentic AI's potential for enhanced efficiency but raises concerns over accountability and ethical risks. Conclusions emphasize the need for balanced integration of both paradigms to optimize AI applications in sectors like healthcare and cybersecurity, contributing to theoretical advancements and practical guidelines for AI deployment. This work bridges gaps in understanding evolving AI typologies, offering insights for policymakers and practitioners.