Core Viewpoint - The article discusses the introduction of FDABench, a benchmark designed for evaluating data agents in heterogeneous data analysis, developed by Nanyang Technological University, National University of Singapore, and Huawei. It aims to address the growing demand for data-driven decision-making by providing a comprehensive assessment framework for data agents across various data types and complexity levels [1][11]. Group 1: Benchmark Overview - FDABench covers over 2007 different test tasks across more than 50 fields, including finance and e-commerce, with three levels of difficulty: easy, medium, and hard [13]. - The benchmark includes a unique Agent-Expert collaboration framework that supports various data agent workflows, ensuring compatibility across different data agent systems without needing to redesign the testing framework [17]. Group 2: Evaluation Findings - The evaluation of various data agent systems revealed unique strengths in response quality, accuracy, latency, and token cost, indicating that each system has its advantages [3]. - Complex data agent architectures, such as Multi-Agent and Reflection, significantly outperform simpler architectures in accuracy for heterogeneous data analysis but at a much higher computational cost, consuming 6 to 20 times more resources [23]. Group 3: Resource Allocation Insights - Different data agent architectures optimize performance by reallocating computational resources; for instance, the Reflection architecture allocates 26-29% of its computation to retry mechanisms for higher quality outputs, while the Planning architecture focuses on efficiency by dedicating 32-35% to the generation phase [23]. - The study highlights the importance of matching model selection with architectural complexity, as some models may perform poorly in complex architectures due to a "double reasoning penalty" effect [23]. Group 4: Practical Implications - The article concludes that there is no perfect data agent; some are faster but struggle with complex tasks, while others are accurate but slow and costly. The choice of a data agent should depend on specific needs [24]. - FDABench serves as a tool to help users identify which system best fits their requirements [25].