AGENTIC AI-POWERED AUTONOMOUS SOFTWARE ENGINEERING FRAMEWORK FOR AUTOMATED CODE GENERATION AND DEBUGGING. - Maheshkar, Jaykumar Ambadas; Vankayala, Himaja; Jakkula, Vamshi Krishna; Raj, Leslie Daniel; Khedekar, Pratik; Laheri, Rohit (Scientific Culture, 2026)
This paper discusses the disruptive nature of agentic AI-based autonomous systems in software engineering, specifically the automated code generation and debugging. The main aim is to analyze the role of agentic AI-based systems, who combine autonomy, reasoning as well as adaptive learning in improving efficiency, accuracy, resilience, governance, and scalability within the contemporary development setting. In the study, the secondary research method is used, as the data synthesizes available academic evidence in the form of journals, surveys, and systematic reviews. Secondary data collection included thematic identification, screening and extraction of suitable literature and thematic analysis was used to classify results into coherence themes that included efficiency gains, debugging accuracy, adaptive resilience, governance mechanisms, and multi-agent scalability. Findings show that language models based on transformers and multi-agent systems enhance the generation of code and decrease the technical debt and increase productivity. Debugging frameworks based on machine learning improve precision by identifying some of the latent patterns of error and reducing the time spent in resolution. Adaptive learning strategies enhance resilience by using fault-tolerant designs and self-repairing algorithms, and the governance systems maintain accountability, transparency, and moral control. The collaboration between agents also facilitates scalability through distributed orchestration and cooperative solutions. All these results make agentic AI a software engineering paradigm shift that integrates both automation and governance-conscientious autonomy to provide robust, explainable, and future-proof solutions. The paper ends by stating that technical resilience, ethical protection, and adaptive resilience should be combined together to achieve the maximum potential of autonomous software engineering.