Imagine asking a question about a complex dataset spread across a table and getting a precise, insightful answer. That's the promise of complex Table Question Answering (TQA), a challenging field in AI. Traditional approaches often hit roadblocks, relying on expensive, closed-source language models or requiring extensive fine-tuning with hard-to-get training data. But what if a team of specialized AI agents could collaborate, each using its unique skills, to tackle these complex queries? That's the innovative idea behind MACT (Multi-Agent Collaboration with Tool Use), a new framework that's changing the TQA game. MACT leverages a team of two open-source AI agents: a "planner" and a "coder." The planner strategizes the steps needed to answer the question, breaking it down into smaller, manageable tasks. The coder then translates these steps into precise Python code to extract and manipulate data from the table. To further boost their power, these agents have access to a toolbox, including a calculator, a Wikipedia search tool, and a Python interpreter, enabling them to perform complex calculations, retrieve external knowledge, and process data effectively. Researchers tested MACT on four challenging TQA datasets, covering diverse domains and reasoning complexities. The results? MACT outperformed existing state-of-the-art systems on three out of four benchmarks, even matching the performance of the much larger and more expensive GPT-4 on two datasets. Surprisingly, MACT achieved this without any fine-tuning, making it incredibly adaptable and easy to deploy across different datasets. But the innovation doesn't stop there. MACT also incorporates an efficiency optimization module, allowing it to take shortcuts for simpler questions. This smart feature can save up to 33% of computation time without sacrificing accuracy. While MACT currently focuses on single-table questions, the future holds exciting possibilities for extending this teamwork approach to multi-table scenarios and multilingual datasets. The research suggests that collaboration, not just individual brilliance, may hold the key to unlocking the full potential of AI for complex data analysis.