Core Viewpoint - AI agents are becoming a significant force in reshaping financial workflows by providing new possibilities for automation, efficiency, and scalability [2] Group 1: Definition and Functionality of AI Agents - AI agents represent a leap in AI system development, capable of executing tasks on behalf of users, understanding user goals, and autonomously completing tasks through logical reasoning and iteration [4] - The rise of AI agents is driven by their ability to perform tasks with unprecedented speed, scale, and complexity, enhancing human capabilities in innovation, research, and strategic thinking [5] Group 2: Interaction with Tools and Data - AI agents typically rely on Large Language Models (LLMs) as their "brains," enabling them to plan, reason, and interact with structured and unstructured data through APIs [6] - The introduction of the Model Context Protocol (MCP) standardizes the interface between LLMs and external tools/services, improving interoperability and reliability [7] Group 3: Importance of Interoperability - The potential of AI agents lies in their ability to enhance industry efficiency by sharing workloads and providing large-scale insights, particularly through specialized agents trained for specific tasks [8] Group 4: Automation Triggers - To achieve higher automation efficiency, companies should identify market data changes or signals that prompt employee actions, transitioning these tasks to be autonomously executed by AI agents [9] Group 5: Data Quality and Accuracy - Clean, well-documented, and clearly labeled data is crucial for the success of AI agents, as erroneous data can lead to widespread operational mistakes [10]