Core Insights - The emergence of AI Agents is transforming service models across various industries by enabling autonomous decision-making and continuous evolution [1][5] - The collaboration between Jincai Hulian and Huawei has led to the development of a financial and tax AI Agent, which enhances compliance management and operational efficiency in the finance and tax sector [2][4] Group 1: AI Agent Technology - AI Agents are based on machine learning and are characterized by their autonomy and adaptability, allowing them to perceive task environments and optimize strategies with minimal human intervention [1][2] - The financial and tax industry faces challenges during digital transformation, including increased compliance pressure and inefficiencies in traditional operations [2][3] - The introduction of AI Agents addresses these challenges by integrating perception, decision-making, and execution capabilities, moving from "tool assistance" to "intelligent agency" [2][3] Group 2: Collaboration and Model Development - Jincai Hulian and Huawei signed a cooperation agreement in September 2023 to develop the "Xinzhi Yue Financial and Tax Model," leveraging Huawei's cloud technology and Jincai Hulian's industry experience [2][4] - The model features an open architecture and a self-developed intelligent scheduling platform, enabling dynamic resource allocation based on various business needs [2][4] Group 3: Application and Impact - The AI Agent covers the entire financial and tax process, automating tasks from invoice processing to tax declaration, significantly reducing manual intervention [3][4] - It can automatically identify anomalies in invoices and analyze tax burden fluctuations, providing actionable insights based on a comprehensive regulatory knowledge base [3][4] - The AI Agent's ability to adapt and learn from user interactions allows it to cater to the specific needs of different industries, enhancing its applicability [4][5] Group 4: Industry Transformation - The collaboration has resulted in significant efficiency gains, reducing monthly report processing time from three days to under four hours and decreasing error rates by over 90% [4] - This model sets a benchmark for the application of AI Agents in other high-regulation sectors, such as finance and government [4][5] - The ongoing evolution of AI Agents towards multi-modal interaction and collaboration among multiple agents signifies a shift from compliance execution to data-driven decision support [5]