Authors: Professor Nikita Bante, Professor Uday Mahure, Professor Prajakta Helonde, Professor Radha Yete, Professor Aachal Aakre
Abstract: The advent of Agentic AI systems—AI entities that possess autonomy, contextual awareness, and adaptive learning capabilities—has revolutionized the landscape of software development. Unlike traditional rule-based automation tools, agentic AI can perform high-level cognitive functions, including code generation, optimization, debugging, and collaborative task execution without constant human oversight. This paper explores the role of agentic AI in automating various phases of the software development lifecycle (SDLC), from requirements gathering to deployment and maintenance. The research highlights the growing integration of Large Language Models (LLMs), multi-agent systems, and self-improving codebases. It discusses how these intelligent agents enhance developer productivity, reduce time-to-market, and minimize manual coding errors. Through a blend of empirical evidence, recent technological advancements, and case studies, the study showcases the operational and strategic implications of adopting agentic AI. It further identifies potential challenges, such as security risks, interpretability, over-reliance, and ethical dilemmas. The goal is to contribute to a better understanding of how agentic systems are reshaping software engineering practices and to offer practical recommendations for integrating these tools in development workflows responsibly and efficiently.
