What It Takes to Scale Agentic Workflows Across the SDLC

Scaling agentic workflows across the software lifecycle requires more than adding coding assistants. This insight explains how AI-native SDLCs distribute work across planning, coding, review, testing, deployment, and monitoring agents while retaining human approval at critical boundaries. It compares orchestration patterns, outlines observability and evaluation needs, and examines the governance, ownership, and reliability roles required to operate multi-agent systems safely. Technology leaders should treat orchestration, traceability, and accountability as shared engineering infrastructure before expanding agent autonomy.