Evolving Argo Workflows for AI: New Features, Tuning, and Multi-Tenant Isolation - S. Tian & Y. Su

Over the past year, Argo Workflows adoption in AI has surged, particularly in China. Key adopters include BYD, which runs autonomous driving pipelines on Argo, and Qwen3-Coder-Next, which models agentic coding tasks as Argo workflows in its technical report. As engineers responsible for scaling Argo Workflows in these production environments, we share our insights from operating it as the control plane for large-scale AI pipelines. We will cover: • Argo Workflows in Emerging Scenarios: Mapping core capabilities and execution models to autonomous driving, LLM fine-tuning, and agentic coding. • New Features and Tuning: Recent community enhancements for large-scale workloads, and practical controller tuning patterns for concurrency and queue management. • Data Isolation and Secure Access: Managing multi-tenant pipelines with per-replica, object-storage-backed volumes, and workflow-aware storage policies so each branch, step, or agent accesses only its own data subset.