MCP/A2A之后，Agent补齐最后一块协议拼图

Core Insights - The introduction of the AG-UI protocol completes the necessary framework for AI application ecosystems, following the MCP and A2A protocols [3][24] - The AI application ecosystem is structured around three roles: users, agents, and the external world, with a focus on interoperability among these roles [2][3] - The trend in AI model training is becoming increasingly oligopolistic, with only a few major players capable of developing foundational large models [1] Group 1: Protocols Overview - MCP and A2A protocols serve as foundational infrastructures for AI applications, facilitating communication between agents and the external world, and between agents themselves [2][9] - AG-UI protocol addresses the communication between users and agents, filling the gap left by MCP and A2A [3][24] - AG-UI provides a standard framework for front-end applications to communicate with back-end agents, enhancing user experience [13][24] Group 2: Agent Functionality - Agents act as intermediaries that perform tasks on behalf of users, similar to real-world agents like real estate brokers [8][9] - The efficiency of agents is highlighted by tools like Lovart, which can autonomously generate video content by coordinating various resources [9][10] - The need for standardized protocols like MCP and A2A arises from the necessity for agents to interact with various tools and each other effectively [9][11] Group 3: AG-UI Protocol Features - AG-UI protocol introduces an event-driven model that allows front-end applications to receive real-time updates from agents, improving user interaction [13][16] - It includes five types of events: Lifecycle Events, Text Message Events, Tool Call Events, State Management Events, and Special Events, which facilitate efficient communication [17][20] - The protocol allows for incremental updates, reducing the need for complete data transfers and enhancing performance [17][22] Group 4: User Experience Enhancement - AG-UI enables front-end applications to provide immediate feedback to users based on agent activity, such as displaying loading indicators during processing [16][22] - The protocol supports a seamless user experience by allowing for real-time updates and interactions without waiting for complete responses [16][22] - By standardizing communication between agents and user interfaces, AG-UI aims to improve the overall efficiency and effectiveness of AI applications [24]