This paper investigates the evolution of financial advisory systems from traditional roboadvisors to autonomous financial agents driven by Large Language Models (LLMs). As the financial industry increasingly embraces artificial intelligence and natural language processing, the role of LLMs in providing sophisticated, context-aware financial advice is gaining prominence. The study explores the development of autonomous financial agents, focusing on their capabilities, limitations and the impact of prompt engineering on their performance.