Abstract
The rapid advancements in artificial intelligence (AI) and large language models (LLMs) have transformed how complex systems process and analyze information. However, in financial transactional contexts, these systems face significant challenges in maintaining contextual awareness, reasoning accurately, and delivering specific insights. This research investigates the integration of knowledge graphs as a foundational component for enhancing context aware search and reasoning in financial AI agents. Knowledge graphs offer structured, interconnected representations of data, allowing the agents to understand relationships, find relevant information, and provide explainable outputs. By leveraging logical reasoning, augmented memory architectures, and retrieval based mechanisms, this study aims to design and evaluate an AI system capable of contextually accurate decision making in financial environments. The proposed research addresses critical issues such as data privacy, system transparency, and bias mitigation while laying a framework of ethical principles for future studies. Through experimentation, this paper will explore how knowledge graphs can optimize search, improve reasoning processes, and enhance the performance of financial agents. The findings aim to contribute to the development of reliable, scalable, and contextually aware AI systems tailored for the financial sector, setting the stage for their broader adoption in real-world applications.