Orchestrating Scalable Software Engineering Workflows: A Reconfigurable Agent-Based Framework for Lifecycle Automation and Cross-Phase Optimization

The challenges behind modern software engineering are large, distributed teams that maintain complex systems with changing requirements, a multi-toolchain, and very limited release intervals. Since the Software Development Lifecycle (SDLC) requires manual coordination, it is prone to mistakes and inefficiencies, resulting in bottlenecks, poor communication, and productivity losses. The paper presents the idea of a scalable end-to-end lifecycle automation based on a reconfigurable agent-based framework that can deal with all principal phases of required engineering, testing, deployment, production, and maintenance. Its runtime supports extensibility, allowing for the easy integration of new tools or agents with minimal disturbance to the architecture. In order to test the framework against two practical enterprise examples with a multi-repo DevOps pipeline and CI constants. According to experimental findings, the tasks have been assembled with improved throughput (21-36%), a decrease in cycle time of up to 28%, and increased traceability in the workflow stages. The results confirm the framework's ability to scale, modularity, and pragmatic suitability in engineering ecosystems of complexity. The piece of work presents an elastic and intelligent model of automation to serve continuous improvement and phase-wise optimization in recent software engineering.