Agentic AI has emerged as a key topic in fintech discussions, especially at the 2025 conference circuit, where it stands out as the most overhyped yet significant technology. It marks a shift in how software systems create value, particularly in risk decisioning, a crucial area for financial services. The term agentic AI goes beyond just being a smarter version of generative AI. It involves a fundamental rethinking of how large language models (LLMs) are structured and utilized, addressing limitations like hallucination—where models generate plausible but incorrect outputs. Agentic AI systems integrate LLMs with additional capabilities such as tool usage, memory, and planning logic. This allows them to perform complex tasks by breaking them down into manageable steps and selecting the appropriate tools or models for each. For instance, the evolution of OpenAI's ChatGPT illustrates this shift. Initially a simple text generator, it now functions as a dynamic agentic system capable of real-time information retrieval, data analysis, and more, all while orchestrating these tasks seamlessly. The potential for agentic AI to enhance risk decisioning in banks and fintech companies lies in its ability to transform how data—both structured and unstructured—is processed for fraud detection, compliance checks, and credit assessments. By leveraging the orchestration capabilities of agentic AI, financial institutions can improve decision-making efficiency and accuracy, moving beyond manual underwriting methods to more sophisticated, automated processes. This evolution is not just about increasing model size; it’s about fundamentally rethinking the architecture of decision-making systems in finance.