Beyond the Copilot: Scaling Agentic Workflows in the AI-Native SDLC

AI-native SDLC changes software delivery because AI agents move from isolated assistants to workflow participants that plan, code, review, test, deploy, and monitor under human oversight. The article explains how multi-agent SDLC models use role-specific agents, orchestration patterns, decision tracing, output evaluation, and accountability practices to manage autonomy and trust. It also highlights organizational shifts such as AI platform engineers, agent reliability engineering, DevEx ownership, and updated governance policies. Technology leaders should treat agentic SDLC as an operating model requiring observability, accountability, and human judgment.