Advancing innovation in financial stability: A comprehensive review of ai agent frameworks, challenges and applications Satyadhar Joshi * Independent Researcher, BoFA, NJ, USA. World Journal of Advanced Engineering Technology and Sciences, 2025, 14(02), 117-126 Publication history: Received on 05 January 2025; revised on 11 February 2025; accepted on 14 February 2025 Article DOI: https://doi.org/10.30574/wjaets.2025.14.2.0071
Abstract Artificial Intelligence (AI) agents are revolutionizing industries by enabling autonomous decision-making, task execution, and multi-agent collaboration. This paper provides a comprehensive review of AI agent frameworks, focusing on their architectures, applications, and challenges in financial services. We conduct a comparative analysis of leading frameworks, including LangGraph, CrewAI, and AutoGen, evaluating their strengths, limitations, and suitability for complex financial tasks such as trading, risk assessment, and investment analysis. The integration of AI agents in financial markets presents both opportunities and challenges, particularly in terms of regulatory compliance, ethical considerations, and model robustness. We examine agentic AI design patterns, multi-agent systems, and the deployment of AI agents advancing the proposal to use them for fraud detection and risk management. By synthesizing insights from academic research and industry practices, this review identifies key trends and future directions in AI agent development. This work contributes to the growing discourse on AI-driven automation by outlining technical considerations and open challenges in deploying AI agents at scale. We highlight the need for enhanced transparency, interpretability, and security in AI-driven Agentic systems. Our findings provide valuable insights for researchers and practitioners seeking to harness AI agents for more efficient and intelligent decision-making.
Keywords: AI Agents; Multi-Agent Systems; Agent Frameworks; Generative AI 1. Introduction The rise of sophisticated AI agents, powered by Large Language Models (LLMs), is transforming various industries, and finance is no exception. These agents, capable of reasoning, planning, and interacting with their environment, offer the potential to automate complex financial tasks, improve decision-making, and create new opportunities. This paper provides a comprehensive overview of AI agents in finance, examining their architectures, frameworks, and applications. AI agents have emerged as a transformative technology, enabling autonomous systems to perform complex tasks across various domains. From financial decision-making to enterprise automation, AI agents are revolutionizing industries by leveraging large language models (LLMs) and multi-agent collaboration [1]. This paper reviews the state-of-the-art in AI agent frameworks, focusing on their architectures, applications, and challenges. AI agents are becoming integral components in automating complex workflows, enhancing financial modeling, and improving risk assessment strategies [2], [3]. These autonomous systems leverage machine learning (ML) and natural language processing (NLP) techniques to optimize decision-making in various industries, particularly finance [4], [5]. The field of Artificial Intelligence (AI) has seen rapid growth in recent years, with AI agents emerging as a prominent area of development. AI agents are autonomous entities capable of perceiving their environment, making decisions, and taking actions to achieve specific goals [1], [6]. These agents are being deployed across various industries, including finance, where they promise to automate tasks, improve decision-making, and enhance overall efficiency. Recent reports from McKinsey [3] and Moody’s Analytics [7] highlight the growing importance of AI agents in transforming business processes. * Corresponding author: Satyadhar Joshi, Satyadhar.Joshi@gmail.com
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0.