Artificial Intelligence Agent Frameworks in Financial Stability: Innovations, Challenges, Applications

Artificial Intelligence (AI) agents are revolutionizing industries by enabling autonomous decision-making, task execution, multi-agent collaboration. This paper provides a comprehensive review of AI agent frameworks, focusing on their architectures, applications, challenges in financial services. We conduct a comparative analysis of leading frameworks, including LangGraph, CrewAI, AutoGen, evaluating their strengths, limitations, suitability for complex financial tasks such as trading, risk assessment, investment analysis. The integration of AI agents in financial markets presents both opportunities challenges, particularly in terms of regulatory compliance, ethical considerations, model robustness. We examine agentic AI design patterns, multi-agent systems, the deployment of AI agents advancing the proposal to use them for fraud detection risk management. By synthesizing insights from academic research industry practices, this review identifies key trends future directions in AI agent development. This work contributes to the growing discourse on AI-driven automation by outlining technical considerations open challenges in deploying AI agents at scale. We highlight the need for enhanced transparency, interpretability, security in AI-driven Agentic systems. Our findings provide valuable insights for researchers practitioners seeking to harness AI agents for more efficient intelligent decision-making.