RT Avi Chawla (@_avichawla)Building front-end Agentic apps just got 10x easier (open-source)!If you're building apps where Agents are part of the interface, not just running in the background, AG-UI protocol has become the standard.For context:- MCP connects agents to tools- A2A connects agents to other agents- AG-UI connects agents to usersIt defines a common interface between Agents and the UI layer.AG-UI itself is Agent framework agnostic, and it lets you:- stream token-level updates- show tool progress ...

RT Avi Chawla (@_avichawla)After MCP, A2A, & AG-UI, there's another Agent protocol (open-source).ACP (Agent Communication Protocol) is a standardized, RESTful interface for Agents to discover and coordinate with other Agents, regardless of their framework (CrewAI, LangChain, etc.).Here's how it works:- Build your Agents and host them on ACP servers.- The ACP server will receive requests from the ACP Client and forward them to the Agent.- ACP Client itself can be an Agent to intelligently route requests to t ...

Agentic Applications - Agentic applications require both Agent-to-Agent communication (A2A) and Machine Control Protocol (MCP) [1] Agent Collaboration - MCP equips agents with tool access [2] - A2A enables agents to connect and collaborate in teams [2]

Group 1 - The core viewpoint of the articles revolves around the rapid development and commercialization of AI agents, particularly following the success of Manus, which has sparked significant interest and investment in this sector [2][3][4]. - Major tech companies are intensifying their efforts in the AI agent space, with ByteDance reportedly forming at least five teams to develop various AI agent products, and Baidu launching the "Xinxiang" app, which aims to compete with Manus [4][5]. - The investment landscape is also shifting, as evidenced by the $75 million funding round for Manus's parent company, Butterfly Effect, which has raised its valuation to nearly $500 million [2]. Group 2 - The emergence of AI agents is seen as a solution to the unmet business needs and technological gaps left by previous enterprise digital transformation efforts [3]. - Companies are adopting the MCP (Multi-Cloud Platform) mechanism to enhance the ecosystem of AI agents, with major players like Alibaba, Tencent, and Baidu integrating MCP protocols into their AI products [6]. - There is a growing concern regarding the safety and risk management of AI agents, as many companies lack a comprehensive understanding of the associated risks, with a significant portion of clients unaware of what AI agents entail [7][8]. Group 3 - The concept of AI agents is evolving, with new terminologies such as Agentic AI and Agentic Workflow gaining traction, indicating a shift towards more specialized and collaborative AI systems [10][11]. - The industry is focused on making AI agents adaptable to complex application scenarios, requiring advancements in perception, understanding, planning, and execution [11][12]. - There is a call for a more cautious approach to the deployment of AI agents, emphasizing the need for improved governance and risk assessment capabilities before widespread implementation [12].

23 年 4 月以 AutoGPT 为代表的那一波里，Agent 更像是一个玩具，demo 都很炫，但实际应用价值很有限。 经过两年的发展，这波 Agent 确实能够在实际的工作和生活场景中解决问题，为大家带来价值了。 曲凯： Agent 是当下绝对的风口。关于 Agent 这个话题，我自己有一些核心在思考的问题，相信也是很多人同样会有疑问的地方。所以今天我们请来了长时间对 Agent 有研究和实操的文锋，想就这些问题展开一些讨论。 首先我想问，到底怎么定义 Agent？ 文锋： 我认为最好的就是 Anthropic 的定义：Agent 是让模型基于环境反馈去使用工具的一个程序。 曲凯： 那你怎么看最近这波 Agent 热？ 文锋： 这波 Agent 跟过去非常不一样。 之所以会有这种跃迁，一是因为底层模型能力有了很大的进步，尤其是在结合了 RL 之后，以 o1 为代表的模型还赋予了 Agent 长思维能力。 二是因为 Agent 的工程侧和产品侧也有很大的突破，主要表现就是大家更知道该怎么给 Agent 构建一个合适的 Context，从而更好地解决问题了。 曲凯： 怎么理解这个 Context？ 文锋： ...