The global financial landscape is undergoing a radical transformation characterized by the convergence of high-capacity computational intelligence and decentralized ledger technologies. This research explores the profound impact of agentic artificial intelligence (AI) and Large Language Models (LLMs) on the evolution of financial services, moving from traditional automated systems to autonomous, self-driven entities capable of complex decision-making. By synthesizing foundational machine learning techniques in credit scoring with contemporary advancements in generative AI, the study identifies a shift from "FinTech" to "TechFin," where data-driven logic precedes financial function. The research delves into the architecture of specialized financial agents, such as FinRobot and StockAgent, examining their capacity for real-time market sentiment analysis and asset allocation. Furthermore, the article investigates the role of confidentiality-preserving blockchain systems in facilitating secure transactions within these agentic frameworks. Through an exhaustive theoretical elaboration, this work addresses the regulatory challenges and ethical considerations of delegated financial autonomy, proposing a synergistic model that integrates machine learning and agent-based modeling to enhance investment accuracy and operational efficiency. The findings suggest that while AI agents significantly augment knowledge work and underlying asset reviews, the future of financial stability depends on robust grounding mechanisms and multidisciplinary regulatory oversight.