AI coding agents have matured from autocomplete tools into genuine software producers, but the path to coherent software at scale runs through orchestration and human oversight—not autonomous YOLO coding. The evidence as of January 2026 is clear: 57% of companies now run AI agents in production, with practitioners like Steve Yegge claiming to produce 12,000 lines of code daily. Yet the quality picture is sobering: Google’s 2025 DORA Report found that 90% AI adoption increase correlates with a 9% climb in bug rates, 91% increase in code review time, and 154% increase in PR size. The technical mechanisms that enable coherent output at scale—hierarchical agent architectures, git-based memory systems, context engineering, and rigorous verification loops—are now well-understood. The fundamental tension between speed and quality remains unresolved.