AI Agents in Financial Markets: Architecture, Applications, and Systemic Implications

TL;DR

This paper introduces an integrative framework for analyzing AI agents in financial markets, focusing on architecture, systemic implications, and an empirical application of agent capability disclosures.

Contribution

It proposes a four-layer architecture for financial AI agents, introduces the Agentic Financial Market Model (AFMM), and provides an exploratory empirical case study.

Findings

AI agent architectures influence market efficiency and stability.

Systemic implications depend on agent distribution, coupling, and governance.

Empirical analysis shows how AI capability disclosures affect market repricing.

Abstract

Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. This paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agent design parameters such as autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk. Third, it presents an illustrative empirical application based on event studies of AI-agent capability disclosures and heterogeneous market repricing. The paper argues that the systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions. The empirical application is intentionally exploratory: it does not validate the full AFMM, but shows how one observable expectations channel can be studied using public data. In the near term, the most plausible equilibrium is bounded autonomy, in which AI agents operate as supervised co-pilots, monitoring systems, and constrained execution modules embedded within human decision processes.