  Creating data stories from raw data is challenging due to humans' limited
attention spans and the need for specialized skills. Recent advancements in
large language models (LLMs) offer great opportunities to develop systems with
autonomous agents to streamline the data storytelling workflow. Though
multi-agent systems have benefits such as fully realizing LLM potentials with
decomposed tasks for individual agents, designing such systems also faces
challenges in task decomposition, performance optimization for sub-tasks, and
workflow design. To better understand these issues, we develop Data Director,
an LLM-based multi-agent system designed to automate the creation of animated
data videos, a representative genre of data stories. Data Director interprets
raw data, breaks down tasks, designs agent roles to make informed decisions
automatically, and seamlessly integrates diverse components of data videos. A
case study demonstrates Data Director's effectiveness in generating data
videos. Throughout development, we have derived lessons learned from addressing
challenges, guiding further advancements in autonomous agents for data
storytelling. We also shed light on future directions for global optimization,
human-in-the-loop design, and the application of advanced multi-modal LLMs.
