Core Viewpoint - The rise of AI Agents is transforming the banking industry, addressing challenges such as diverse customer demands, high operational costs, and intense competition from peers and cross-industry players [3][4]. Group 1: AI Agent's Role in Banking - AI Agents combine the capabilities of large language models (LLMs) and robotic process automation (RPA), enabling autonomous planning, decision-making, and execution, thus transitioning from a "co-pilot" to a primary "driver" role in banking operations [5]. - AI Agents are not merely efficiency tools; they represent a disruptive force that can deeply reconstruct management and service operations within banks, facilitating a shift from "automation" to "autonomy" [5][6]. Group 2: Enhancing Customer Service - AI Agents enhance customer experience by providing 24/7, multi-channel interactions, delivering highly differentiated, personalized, and responsive financial services [8]. - For instance, WeBank utilizes AI Agents to summarize customer dialogue, helping service representatives quickly identify user issues, thereby improving service efficiency and quality [8]. Group 3: Data Governance Improvement - AI Agents address traditional banking challenges such as data silos and low data quality by autonomously identifying, cleaning, and standardizing heterogeneous data, thus enhancing data governance [9]. - A leading city commercial bank collaborated with a technology firm to implement AI Agents for data governance, achieving over 90% accuracy in intelligent querying and over 98% in user intent recognition [9]. Group 4: Connecting Business Systems - AI Agents serve as "digital connectors," integrating various independent business information systems within banks, thereby breaking down silos and improving information transfer efficiency [10]. - For example, Beijing Bank enhanced its AI platform's connectivity using AI Agents, facilitating rapid integration with existing business systems and promoting intelligent interaction and collaboration across systems [10].