Imagine effortlessly querying complex documents and databases simultaneously, getting precise answers in seconds. That's the promise of a new multi-agent orchestration and retrieval methodology using Large Language Models (LLMs). This innovative approach tackles the challenge of integrating information from diverse sources like PDFs and SQL databases, a common hurdle in fields like contract management. Traditional systems struggle to connect the dots between different data formats, often leading to inaccurate or incomplete answers. This new method employs specialized agents – think of them as expert retrievers – that dynamically choose the best retrieval strategy for each question. A "router agent" acts as the brain, deciding whether to use a Retrieval-Augmented Generation (RAG) agent for unstructured text like PDFs or a SQL agent for structured database queries. Dynamic prompt engineering further refines the process, tailoring the query context in real-time for maximum accuracy. Tested in a real-world contract management system, this approach significantly improved response accuracy and relevance. Users praised its ability to seamlessly blend information from different sources, dramatically reducing research time. The system even automatically generates helpful visuals for data-heavy responses. While promising, challenges remain. Improving the router agent's decision-making and expanding support for other data sources are key next steps. This research opens doors to a future where accessing and understanding information, regardless of its format, is faster, easier, and more insightful.