Agentic Artificial Intelligence in Finance: A Comprehensive Survey

2026-04-23

AI Summary

This study addresses the dual role of autonomous AI agents in finance, which enhance market efficiency yet introduce systemic risks requiring urgent clarification of their distinct mechanisms and regulatory pathways. It systematically delineates, for the first time, the fundamental differences between autonomous AI agents in financial contexts and both traditional algorithmic trading and generative AI, emphasizing their paradigm-shifting characteristics: goal-directed behavior, continuous learning, and multi-agent coordination. Through an integrated approach combining literature review, architectural analysis, multi-agent modeling, and regulatory framework evaluation, the paper comprehensively elucidates their potential in improving liquidity and risk management while identifying critical challenges related to market stability, explainability, and regulatory compliance. The work thereby offers a theoretical roadmap to guide future research and inform effective regulatory practice.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Artificial General Intelligence
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The emergence of agentic artificial intelligence (AI) represents a fundamental transformation in financial markets, characterized by autonomous systems capable of reasoning, planning, and adaptive decision-making with minimal human intervention. This comprehensive survey synthesizes recent advances in agentic AI across multiple dimensions of financial operations, including system architecture, market applications, regulatory frameworks, and systemic implications. We examine how agentic AI differs from traditional algorithmic trading and generative AI through its capacity for goal-oriented autonomy, continuous learning, and multi-agent coordination. Our analysis shows that while agentic AI offers substantial potential for enhanced market efficiency, liquidity provision, and risk management, it also introduces novel challenges related to market stability, regulatory compliance, interpretability, and systemic risk. Through a systematic review of foundational research, technical architectures, market applications, and governance frameworks, this survey provides scholars and practitioners with a structured understanding of how agentic AI is reshaping financial markets and identifies critical research directions for ensuring that these systems enhance both operational efficiency and market resilience.

Problem

• agentic AI
• financial markets
• systemic risk
• regulatory compliance
• market stability

Innovation

• agentic AI
• autonomous decision-making
• multi-agent coordination
• financial market transformation
• systemic risk