Agentic AI: A Quantitative Analysis of Performance and Applications

Manuscript submitted March 13, 2025; accepted April 7, 2025; published May 20, 2025

Abstract—This study presents a comprehensive quantitative analysis of Agentic AI performance and applications across various industries. Agentic Artificial Intelligent (AI), an emerging field combining advanced AI techniques with enterprise automation, has shown promise in creating autonomous agents capable of complex decision-making and problem-solving. Our research, conducted over a 12-month period, employed a mixed-methods approach, analyzing data from 500 organizations and incorporating insights from 50 industry experts. The study aimed to evaluate the efficiency, accuracy, and impact of Agentic AI systems compared to traditional AI approaches. Results demonstrate that Agentic AI systems significantly outperform traditional AI, with a 34.2% reduction in task completion time, 7.7% increase in accuracy, and 13.6% improvement in resource utilization. Productivity gains varied across industries, with the technology sector showing the highest improvement at 45%. The study also revealed high scalability of Agentic AI solutions across different organizational sizes, although implementation time increased with organization complexity. Key challenges identified include data privacy concerns, integration difficulties with legacy systems, skill gaps, and ethical considerations. Despite these challenges, the study concludes that Agentic AI has significant potential to transform business processes and decision-making across various sectors. Future research directions include enhancing interpretability, optimizing domain-specific applications, and exploring multi-agent collaborations. This research contributes valuable insights into the current state and future prospects of Agentic AI, providing a foundation for further development and implementation strategies in this rapidly evolving field.

keywords—Artificial intelligence, agentic Artificial Intelligent (AI), advanced AI techniques, multi-agent collaborations

Cite: Prashant D. Sawant,"Agentic AI: A Quantitative Analysis of Performance and Applications," Journal of Advances in Artificial Intelligence, vol. 3, no. 2, pp. 132-140, 2025. doi: 10.18178/JAAI.2025.3.2.132-140

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).