Core Viewpoint - The article discusses the transition from traditional AI infrastructure to a new paradigm called "Agentic Infra," which is essential for the scalable deployment of intelligent agents in various industries [2][3]. Infrastructure Evolution - The evolution of infrastructure is moving from AI Infra to Agent Infra and then to Agentic Infra, which is crucial for the large-scale implementation of intelligent agents [2]. - The infrastructure must evolve from a "production line factory" to a "solution company" to support the quality of tasks executed by agents [3][4]. Key Upgrades Required - Multiple dimensions need to be upgraded, including flexible execution environments, comprehensive tools for agents, precise contextual information, and robust security and monitoring mechanisms [4]. - The infrastructure must coordinate continuous and interrelated tasks, emphasizing the importance of sandboxing and flexible scheduling capabilities [4]. Shift in Focus - The focus has shifted from "calculating faster" to "thinking longer," requiring different types of resources for thinking and calculation [5]. - The current bottleneck lies not in the models themselves but in the supporting infrastructure's responsiveness [6]. Challenges in Agent Deployment - The decline in user numbers for platforms like Lovable indicates that while initial interest may be high, sustained engagement is challenging due to unmet user expectations [5]. - The core issue is that while agent models are capable, the supporting infrastructure and tools are still immature [6]. Future of Agentic Infra - The goal is to create an advanced Agentic Infra that allows for better resource integration and innovative functionalities, leading to a virtuous cycle of technology and application development [7][10]. - The infrastructure should enable agents to autonomously design workflows, moving from being viewed as tools to collaborators [12][13]. Technical Innovations - The introduction of micro-virtualization and sandbox management mechanisms aims to optimize resource allocation and utilization, addressing inefficiencies in traditional AI infrastructure [16]. - Unified scheduling of heterogeneous computing resources is a key innovation, allowing for better performance and efficiency [17][18]. Industry Integration - The transition from technical breakthroughs to industry integration is crucial, focusing on usability and performance rather than underlying hardware differences [18]. - The company aims to provide a robust AI-native infrastructure that supports clients in focusing on product iteration while managing complex backend operations [19][20]. Vision for the Future - The vision includes a future where intelligent agents collaborate to complete complex tasks, significantly enhancing productivity and creativity [14][22]. - The company aspires to be a foundational engine for AGI development, facilitating the transition to a more intelligent and autonomous infrastructure [22].

Next, repeat these steps for the 2nd server to host the Smolagents Agent and its LLM.- Line 1-10 → Imports + define the Server & the LLM.- Line 12 → Decorate the method.- Line 21-28 → Define the Agent with a web search tool.- Line 31 → Serve the Agent.Finally, we use an ACP client to connect both agents in a workflow.- Line 6-7 → Connect the client to both servers.- Line 11-14 → Invoke the first agent to receive an output.- Line 18-21 → Pass the output to the next agent for enhancement.Next, run the two ser ...

Industry Collaboration & Standardization - Google's "Agent Payments Protocol" (AP2) is an open standard facilitating AI agent payments across platforms and crypto rails [1] - AP2 builds upon A2A & MCP, enabling seamless communication between agents and real-world data [2][3] Key Players & Integrations - MetaMask is potentially integrating agents to enhance user experience [1] - Ethereum is highlighting its intent to embrace AI [1] - Coinbase's x402 (Base's baby) supports payments via cards, bank transfers, local methods, and crypto [2] - EigenLayer provides validation/verification support [2] - Crossmint offers flexibility for agents to use fiat or crypto [3] - Mysten Labs' Sui infra could see deeper Sui AI agent rollout [3] Infrastructure & Connectivity - Mesh provides infrastructure connecting wallets, exchanges, and tokens [2] Future Outlook - The industry anticipates a surge in attention around AI within the next 12 months [3] - The future is Agentic and will happen permissionlessly on crypto rails [2]

Investment Rating - The report maintains an "Outperform" rating for the AI industry [1] Core Insights - AI Agents represent a significant evolution in AI technology, moving beyond simple command execution to autonomous decision-making and task execution, achieving performance levels equivalent to 90% of skilled adults [3][10] - The AI infrastructure is undergoing a transformation, with major cloud providers like Microsoft, Google, and Amazon enhancing their AI/Agent platforms to capture new market opportunities [3][51] - The global AI IT spending is projected to grow at a CAGR of 22.3% from 2023 to 2028, with Generative AI (GenAI) expected to account for 73.5% of this growth [3] Summary by Sections 01 Agent Definition, Technology, and Development - AI Agents are defined as intelligent entities with autonomy, planning, and execution capabilities, surpassing traditional automation [10] - Key features include autonomous decision-making, dynamic learning, and cross-system collaboration [10] 02 Agent Development Platform Layout - Major players in the AI Agent development space include Microsoft, Google, Amazon, Alibaba, and Tencent, each with distinct strategies and market focuses [3][51] 03 Model Layer and Tokens Usage Analysis - The report highlights the rapid increase in token usage, with Google's Gemini model projected to reach 980 trillion tokens by July 2025, a 100-fold increase from the previous year [3] - Domestic models like Byte's Doubao are also seeing significant growth, with daily token usage expected to reach 16.4 trillion by May 2025, a 137-fold increase [3] 04 C-end and B-end Agent Progress - C-end applications are heavily reliant on model capabilities, with significant growth in image and programming-related products [3] - B-end applications, such as Microsoft's Copilot, have over 100 million monthly active users, but face challenges related to data security and cost [3] Agent Market Size and Development Expectations - The AI Agent market is expected to reach $103.6 billion by 2032, growing at a CAGR of 44.9% [3] - The report anticipates that by 2035, AI Agents will become mainstream as cognitive companions for humans [3]

AG-UI Protocol Overview - AG-UI protocol has become the standard for building front-end Agentic apps where Agents are part of the interface [1] - AG-UI defines a common interface between Agents and the UI layer, remaining Agent framework agnostic [2] Key Features of AG-UI - AG-UI enables streaming token-level updates, showing tool progress in real time, sharing mutable state, and pausing for human input [2] - Developers can spin up a full-stack AG-UI app directly from CLI and visualize A2A interactions [2] - Pydantic AI is now AG-UI compatible [2] Development Efficiency - Building AG-UI frontends is now 10x faster with a plug-and-play interface [1][2] - A fully revamped contributor flow is available for developers [2] Agent Connectivity - MCP connects agents to tools, A2A connects agents to other agents, and AG-UI connects agents to users [2]

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： ...