Eliminating Human Intervention in the Software Development Lifecycle: A Novel Agentic Paradigm for Scalable Infrastructure Maintenance

Abstract

The persistent requirement for human intervention within software development and maintenance workflows has become the defining productivity bottleneck in modern platform engineering. Despite dramatic advances in AI-assisted code generation, the downstream lifecycle stages of build triage, code review, test authoring, and production incident response continue to impose a linear scaling constraint on engineering organizations: increasing software output requires proportional increases in human labor. This article presents a comprehensive Autonomous Software Engineering framework that deploys AI agents across all phases of the Software Development Lifecycle (SDLC), from requirement synthesis and autonomous code generation through self-healing build repair, AI-driven code review, generative test creation, and autonomous production monitoring. The framework leverages Model Context Protocol (MCP) interfaces to grant agents structured access to CI/CD pipelines, code repositories, test infrastructure, and operational telemetry, enabling end-to-end lifecycle automation without human intermediation for routine operations. Empirical evidence from large-scale deployments indicates that autonomous triage systems can reclaim 30 to 40 percent of engineering bandwidth currently consumed by environmental maintenance, while AI-driven code review reduces human review latency by up to 40 percent. The article examines the architectural requirements, operational risks, and organizational implications of this transition, with particular attention to the governance mechanisms required to maintain quality and safety in fully automated pipelines.