This research introduces innovative methodologies for improving financial retrieval-augmented generation systems through the integration of agentic AI approaches and Multi-HyDE (Multi-Hypothesis Document Embedding) techniques. The work addresses critical challenges in financial information retrieval, particularly focusing on reducing hallucinations in large language models and enhancing the accuracy of generated responses in financial contexts. The proposed system demonstrates significant improvements in information retrieval precision and reliability for financial applications.