Agentic AI Frameworks for Autonomous Enterprise Software Development Workflows
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P122Keywords:
Agentic AI, Autonomous Systems, Enterprise Engineering, Multi-Agent Systems, AI Workflow AutomationAbstract
Enterprise software engineering has grown at a fast pace, leading to an ever increasing need for intelligent, adaptive and autonomous development frameworks that can meet the ever increasing complexity of application design, implementation, testing, deployment and maintenance. With recent developments in autonomous reasoning systems, large language models, and multi-agent orchestration architectures, a new paradigm in enterprise software development workflows has emerged: Agentic Artificial Intelligence (Agentic AI). Agentic AI goes beyond AI-assisted programming, allowing AI systems to autonomously decompose their work, make decisions based on context, reason together, use tools dynamically, and learn continuously. This paper offers a full picture of Agentic AI-powered autonomous enterprise software development processes. The study explores intelligent software agents' coordination in the software development lifecycle phases of requirement engineering, architecture design, code generation, security validation, test automation, deployment orchestration, and post-deployment optimization. The suggested framework features multi-agent collaboration, retrieval-augmented generation, secure orchestration pipelines, reinforcement learning for adapting software, and enterprise governance to facilitate scalable, trusted software delivery. Our literature search reveals that, in general, there are many developments in the field of AI-assisted DevOps, autonomous software engineering, and enterprise workflow automation that have taken place since February 19, 2025. But there are still challenges with interoperability, governance, trust, explainability and real-time decisions adaption. This paper suggests a layered Agentic AI architecture with planning agents, execution agents, validation agents, governance agents, and feedback-learning agents to fill these gaps. Experimental evaluation in simulated enterprise development environments shows that quality of code, reductions of defects, deployment speed, developer productivity and compliance assurance are measurable. The results show that the productivity gains are 38%, defect reduction 41%, release cycle gain 47%, and security compliance gain 35% compared to conventional AI-assisted pipelines. The results validate the Agentic AI frameworks as a new paradigm in enterprise software engineering, shifting the paradigm from human-in-the-loop towards human-governed autonomous development environments.
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