The rapid evolution of artificial intelligence has ushered in a transformative era for quantitative investment

driven by the emergence of autonomous AI agents. This study investigates application paradigms

challenges

and future directions of AI agents in quantitative finance through empirical analysis. We identify three core deployment frameworks: foundation models

fine-tuned systems

and retrieval-augmented generation (RAG). These frameworks enable AI agents to enhance efficiency across the investment pipeline

including signal generation

portfolio optimization

and order execution. Key innovations include multimodal data integration and human-AI collaboration. However

challenges such as integration complexity and model interpretability hinder widespread adoption. We propose that advancements in hybrid architectures and explainable AI will accelerate deployment.