Tabular Question Answering (TabQA) is a challenging task that requires models to comprehend structured tabular data and generate accurate responses based on complex reasoning. In this paper, we present our approach to SemEval Task 8: Tabular Question Answering, where we develop a large language model (LLM)-based agent capable of understanding and reasoning over tabular inputs. Our agent leverages a hybrid retrieval and generation strategy, incorporating structured table parsing, semantic understanding, and reasoning mechanisms to enhance response accuracy. We fine-tune a pre-trained LLM on domain-specific tabular data, integrating chain-of-thought prompting and adaptive decoding to improve multi-hop reasoning over tables. Experimental results demonstrate that our approach achieves competitive performance, effectively handling numerical operations, entity linking, and logical inference. Our findings suggest that LLM-based agents, when properly adapted, can significantly advance the state of the art in tabular question answering.