Explore how autonomous AI agents are transforming financial markets and decision-making   In  Agentic Finance: Mathematics, Architecture, and Governance for AI Agents in Finance,  Miquel Noguer Alonso and David Pacheco Aznar examine the emerging field where autonomous AI systems operate independently in financial contexts. The book explores how agentic technologies – AI systems capable of independent reasoning, planning, and action – are reshaping how financial institutions approach analysis, trading, and risk management.  The book explores the mathematical foundations powering autonomous AI agents, from the algorithms enabling independent decision-making to the frameworks governing multi-agent financial systems. Readers gain insight into how innovators are applying these technologies across trading, portfolio management, and market analysis, along with the technical principles driving this transformation.  Readers will also discover:  The core mathematical concepts underlying autonomous AI systems, including reinforcement learning, optimization, and probabilistic reasoning frameworks  How leading innovators are deploying agentic AI across trading, risk assessment, portfolio optimization, and financial forecasting applications  Technical architectures enabling AI agents to operate autonomously while maintaining appropriate oversight and governance in financial environments  Real-world applications demonstrating how autonomous systems analyze markets, execute strategies, and adapt to changing conditions  The evolving regulatory and ethical landscape surrounding autonomous AI decision-making in high-stakes financial contexts  Agentic Finance  serves quantitative analysts, financial technologists, AI researchers, portfolio managers, and business leaders seeking to understand how autonomous AI systems are transforming financial services. It provides the technical foundation needed to evaluate and implement agentic technologies.