A Practical Guide to AI Workflow Graphs

This article presents a practical guide to AI workflow graphs, explaining how they add structure and reliability to probabilistic models by defining nodes, edges, and shared states for tasks ranging from code to human review. It details five common graph patterns like chains and routers while emphasizing best practices such as implementing checkers, guardrails, and limiting loops to ensure quality control. Finally, the authors illustrate these concepts with a 3C positioning graph example, concluding that deliberate topology provides necessary granularity when simpler methods fall short.