The AI-Driven Development Lifecycle: Replacing Over-Planning with Rapid Iteration and Agentic Execution

Abstract

The fast development of AI has provided opportunities to reshape the traditional approaches to software engineering beyond the automation of tasks. The Software Development Lifecycle (SDLC) practices that have existed in the past are still based on massive planning, orderly sequential execution, and decision-making processes, which lead to sluggish delivery and adaptation capabilities. This study presents the AI-Driven Development Lifecycle (AI-DLC), a new approach to software development that substitutes planning-filled processes with fast development and agentic behavior. The suggested model introduces an AI maturity spectrum that includes traditional SDLC, AI-assisted development, AI-driven development and AI-managed development models. Moreover, AI-DLC includes five-stage lifecycle that includes business intent capture, collected contextual knowledge, AI-supported planning, deployment, and continuous improvement. It suggests a multi-agent architecture, which combines design, development, and security, testing, DevOps, and mail industry-specific agents. The framework provides a human-AI operating model wherein human controls are made in strategic decisions but operational processes are run through AI. The assessment system evaluates the speed of delivery, efficiency in engineering, preparedness, software reliability and automation potential. The study is the base of the future self-governing software engineering environments.