The Rise of Agentic AI in Software Engineering: Transforming the Software Development Lifecycle (SDLC)

Agentic Artificial Intelligence (AI) is an emerging approach in which AI systems can plan tasks, use tools, maintain context, and executemulti-step workflows with varying levels of autonomy. In software engineering, these capabilities may reshape the SoftwareDevelopment Lifecycle (SDLC), from requirements analysis and design to coding, testing, deployment, and maintenance. This paperexamines how agentic AI can support development teams through task decomposition, repository-aware code generation, test creation,defect investigation, documentation, and operational feedback. It presents a conceptual architecture in which an orchestrator coordinatesspecialist agents and tools, while policy checks and human review govern consequential actions. The paper compares traditionalautomation, AI-assisted coding, and agentic workflows; identifies potential benefits and risks; and proposes an evaluation frameworkbased on quality, productivity, reliability, security, and review effort. Agentic systems can reduce repetitive work and acceleratefeedback, but they may also produce incorrect changes, expose sensitive data, or take unsafe actions when permissions and validation areweak. Accordingly, the proposed approach treats autonomy as bounded and reversible, uses tests and policy gates, and preserves humanaccountability. This paper is a conceptual design and literature-informed discussion; it does not claim experimental results from adeployed system.