LlamaIndex has introduced a new beta feature called workflows, designed to orchestrate actions in complex AI applications by transitioning from a graph-based to an event-driven architecture. This shift addresses limitations encountered with Directed Acyclic Graphs (DAGs), such as the inability to perform loops and the complexity of handling edge cases in AI systems. Workflows in LlamaIndex are composed of steps, which are essentially Python functions that process and relay events, allowing for more intuitive and flexible orchestration. This architecture facilitates handling loops, maintaining global state, and customizing predefined workflows without starting from scratch. It also supports debugging through visualization tools and manual execution, enhancing the development and maintenance of AI applications. Despite being in early stages, LlamaIndex workflows offer advanced features like asynchronous execution, step-by-step debugging, and easy customization through event-driven dependencies, representing a significant improvement over previous query pipelines.