Sequoia Capital 在 2026: This is AGI 这篇文章中断言 AGI 就是把事情搞定（Figure things out）的能力。 如果说过去的 AI 是 Talkers 的时代，那么 2026 年则是 Doers 的元年。转变的核心载体正是 Long Horizon Agents（长程智能体）。这类 Agent 不再满足于 对上下文的即时回复，而是具备了自主规划、长时间运行以及目标导向的专家级特征。从 Coding 到 Excel 自动化，原本在特定垂直领域爆发的 Agent 能 力，正在向所有复杂任务流扩散。 作为 LangChain 的创始人，Harrison Chase 一直处于这场变革的最前沿。本文编译了 Sequoia Capital Sonya Huang & Pat Grady 访谈 Harrison Chase 的最新播 客。作为站在 Agent 基础设施最前沿的先行者，Harrison 揭示了为什么 Agent 正迎来其爆发的"第三个拐点"。 核心 Insight 提炼： •Long Horizon Agents 价值在于为复杂任务提供高质量初稿； •Agent ...

编译：Arlene、Haina Sequoia Capital 在 2026: This is AGI 这篇文章中断言 AGI 就是把事情搞定（Figure things out）的能 力。 如果说过去的 AI 是 Talkers 的时代，那么 2026 年则是 Doers 的元年。转变的核心载体正是 Long Horizon Agents（长程智能体）。这类 Agent 不再满足于对上下文的即时回复，而是具备了自主规 划、长时间运行以及目标导向的专家级特征。从 Coding 到 Excel 自动化，原本在特定垂直领域爆 发的 Agent 能力，正在向所有复杂任务流扩散。 作为 LangChain 的创始人，Harrison Chase 一直处于这场变革的最前沿。本文编译了 Sequoia Capital Sonya Huang & Pat Grady 访谈 Harrison Chase 的最新播客。作为站在 Agent 基础设施最前沿的先行 者，Harrison 揭示了为什么 Agent 正迎来其爆发的"第三个拐点"。 核心 Insight 提炼： • Long Horizon Agents 价值在于为复杂 ...

Core Insights - The article discusses the transition from Cursor to Claude Code by Silen Naihin, a top user in the AI programming community, highlighting the evolution of AI programming tools and their impact on developers [2][3][4]. Group 1: Transition to Claude Code - Silen Naihin, a prominent user of Cursor, decided to switch to Claude Code after the release of Claude Code 2.0, which he found to be a significant improvement over previous versions [2][15]. - The release of Claude Code 2.0 marked a paradigm shift in AI programming, allowing users to focus on testing behaviors rather than scrutinizing every line of code [18][19]. - Naihin's experience with Claude Code led him to create complex projects without writing any code, showcasing the tool's capabilities [19][20]. Group 2: Features and Advantages of Claude Code - Claude Code offers an asynchronous-first workflow, which encourages users to move beyond traditional code review practices, enhancing productivity [20][21]. - The Opus 4.5 model integrated into Claude Code performs better in its environment, optimizing file searches and tool usage [21][22]. - The tool is cost-effective and customizable, providing an open environment for developers to innovate [23]. Group 3: Practical Insights and Techniques - Naihin emphasizes the importance of context management to avoid hitting the 200k token limit in Claude Code, suggesting strategies to maintain focus on tasks [28][29]. - Planning is crucial; spending time on planning can save significant time in execution, with tools available to facilitate this process [30][31]. - Automation is encouraged for repetitive tasks, and users are advised to validate behaviors rather than reviewing code line by line [32][34]. Group 4: Advanced Strategies and Tools - Naihin outlines five pillars essential for mastering Claude Code, including context management, planning, automation, verifiability, and debugging [25][36]. - He provides specific commands and resources to streamline the setup and usage of Claude Code, enhancing user experience [26][49]. - The article also discusses tailored strategies for different domains, such as frontend and backend development, to maximize the effectiveness of Claude Code [44][46].

Core Viewpoint - Manus represents a significant paradigm shift in AI applications, transitioning from merely generating content to autonomously completing tasks, marking a "DeepSeek moment" in the industry [6][7]. Group 1: Manus's Unique Model - Manus has created over 80 million virtual computer instances, which are crucial to its operational model, allowing AI to autonomously handle complex tasks [9][10]. - This model signifies a shift in core operators from humans to AI, establishing Manus as an "artificial intelligence operating system" [11]. - The Manus model is expected to lead to a 0.5-level leap in human civilization, as AI takes over digital economy-related jobs [12]. Group 2: AI Application's "DeepSeek Moment" - Manus achieved an annual recurring revenue (ARR) of over $100 million within a year, indicating its strong market performance [20]. - The introduction of multi-agent systems has shown a 90.2% performance improvement in handling complex tasks compared to single-agent systems, emphasizing the importance of collaboration among AI [14][17]. - The transition from AI as a tool to AI as a worker signifies a major evolution in AI applications, moving beyond the "toy" and "assistant" phases [20]. Group 3: Technological Foundations of Multi-Agent Systems - Manus's multi-agent system relies on several core technologies, including virtual machines for secure execution environments and resource pooling for efficient resource utilization [22][24]. - The virtual machine architecture allows for independent task execution, addressing safety and reliability issues in AI applications [25]. - Intelligent orchestration ensures optimal resource allocation and task management, enhancing overall system efficiency [26][27]. Group 4: Competitive Landscape and Industry Dynamics - Major tech companies are rapidly advancing in multi-agent systems, with Meta, Google, Microsoft, and Amazon all integrating these capabilities into their platforms [30][32]. - In the domestic market, companies like Alibaba, Tencent, and Baidu are also making significant strides in developing multi-agent technologies [31]. - The emergence of new players like Kimi, which has raised $500 million for multi-agent system development, indicates a growing competitive landscape [33]. Group 5: Evolution of Human Roles - The relationship between humans and AI is shifting from operator-tool dynamics to manager-team dynamics, where humans define tasks while AI executes them [35]. - This evolution will likely reduce the demand for lower and mid-level creative jobs while amplifying the value of high-level creative work [37]. - The traditional hierarchical structure of organizations may flatten as multi-agent systems can handle the entire workflow from strategy to execution [38]. Group 6: Underestimated Risks - Data ownership and system security are critical concerns in multi-agent systems, as data becomes a currency for AI collaboration and system evolution [40][41]. - The complexity of multi-agent systems introduces new security challenges, including process safety, collaboration safety, and evolution safety [42][43]. - Balancing security and efficiency remains a fundamental challenge, as overly secure systems may hinder performance while efficient systems may expose vulnerabilities [44]. Group 7: Irreversible Development Path - The proliferation of Manus's 80 million virtual machines signals a new era of productivity, redefining the nature of work itself [47]. - In the short term, vertical applications of multi-agent systems are expected to explode across various industries, leading to intense market competition [48]. - Over the long term, human-AI collaboration will evolve into a more integrated system, blurring the lines between human and machine contributions [49].

Core Insights - The rise of AI tools, such as ChatGPT and DeepSeek, has significantly increased interest in artificial intelligence, with applications in data analysis and business opportunity identification [1][10] - Large models and intelligent agents are the two key technologies driving this AI revolution, fundamentally changing work and daily life [1][10] Group 1: Large Models - Large models are deep learning models trained on vast amounts of data, characterized by a large number of parameters, extensive training data, and significant computational resources [1][4] - These models provide powerful data processing and generation capabilities, serving as the foundational technology for various AI applications [3][4] - Major global large models include OpenAI's GPT-5, Google's Gemini 2.0, and domestic models like Baidu's Wenxin Yiyan 5.0 and Alibaba's Tongyi Qianwen 3.0, which continue to make breakthroughs in multimodal and industry-specific applications [3][4] Group 2: Intelligent Agents - Intelligent agents, powered by large language models, are capable of proactively understanding goals, breaking down tasks, and coordinating resources to fulfill complex requirements [5][7] - Examples of intelligent agents include OpenAI's AutoGPT and Baidu's Wenxin Agent, which can handle various tasks across different scenarios [7][9] - The micro-financial AI assistant, Weifengqi, utilizes a self-developed financial model to address challenges in the financial sector, transitioning services from labor-intensive to AI-assisted [9] Group 3: Synergy Between Large Models and Intelligent Agents - The relationship between large models and intelligent agents is analogous to the brain and body, where large models provide cognitive capabilities and intelligent agents enable actionable outcomes [10] - The integration of intelligent agent functionalities into AI products is becoming more prevalent, indicating a shift from novelty to practical assistance in daily life [10] - The ongoing development of AI technologies raises considerations such as data security, but the wave of innovation led by large models and intelligent agents presents new opportunities for individuals and businesses [10]

Core Viewpoint - The article discusses the evolution and differentiation between AI Agents and Agentic AI, highlighting their respective roles in automating tasks and collaborating on complex objectives, with a focus on the advancements since the introduction of ChatGPT in November 2022 [2][10][57]. Group 1: Evolution of AI Technology - The development of AI technology has progressed from early expert systems like MYCIN to modern AI Agents and Agentic AI, marking a significant paradigm shift in capabilities [10][11]. - ChatGPT's release in November 2022 is identified as a pivotal moment that catalyzed the evolution of AI Agents, transitioning from passive responders to more autonomous systems capable of executing multi-step tasks [12][24]. - The introduction of frameworks like AutoGPT and BabyAGI in 2023 signifies the formal establishment of AI Agents, which integrate LLMs with external tools to perform complex tasks [12][24]. Group 2: Characteristics of AI Agents - AI Agents are defined as modular systems driven by LLMs and LIMs, designed for task automation, filling the gap where generative AI lacks execution capabilities [13][16]. - Three core features distinguish AI Agents from traditional automation scripts: autonomy, task-specificity, and reactivity [16][17]. - The integration of tools allows AI Agents to overcome limitations of static knowledge and hallucination issues, enabling them to perform real-time data retrieval and processing [19][20]. Group 3: Agentic AI and Multi-Agent Collaboration - Agentic AI represents a shift towards multi-agent collaboration, where multiple AI Agents work together to achieve complex goals, enhancing system-level intelligence [24][27]. - The architecture of Agentic AI includes dynamic task decomposition and shared memory, facilitating efficient collaboration among specialized agents [33][36]. - Real-world applications of Agentic AI demonstrate its advantages in various fields, such as healthcare and agriculture, where multiple agents coordinate to optimize processes [37][38]. Group 4: Challenges and Future Directions - Both AI Agents and Agentic AI face challenges, including causal reasoning deficits and coordination issues among multiple agents [48][50]. - Proposed solutions include enhancing retrieval-augmented generation (RAG), implementing causal modeling, and establishing shared memory architectures to improve collaboration and decision-making [49][53]. - The future roadmap emphasizes the need for deeper causal reasoning, transparency in decision-making, and ethical governance to ensure the responsible deployment of AI technologies [56][59].

Core Viewpoint - The article discusses the evolution and differentiation between AI Agents and Agentic AI, highlighting their respective roles in automating tasks and collaborating on complex objectives, with a focus on the advancements since the introduction of ChatGPT in November 2022 [2][10][57]. Group 1: Evolution of AI Technology - The emergence of ChatGPT in November 2022 marked a pivotal moment in AI development, leading to increased interest in AI Agents and Agentic AI [2][4]. - The historical context of AI Agents dates back to the 1970s with systems like MYCIN and DENDRAL, which were limited to rule-based operations without learning capabilities [10][11]. - The transition to AI Agents occurred with the introduction of frameworks like AutoGPT and BabyAGI in 2023, enabling these agents to autonomously complete multi-step tasks by integrating LLMs with external tools [12][13]. Group 2: Definition and Characteristics of AI Agents - AI Agents are defined as modular systems driven by LLMs and LIMs for task automation, addressing the limitations of traditional automation scripts [13][16]. - Three core features distinguish AI Agents: autonomy, task specificity, and reactivity [16][17]. - The dual-engine capability of LLMs and LIMs is essential for AI Agents, allowing them to operate independently and adapt to dynamic environments [17][21]. Group 3: Transition to Agentic AI - Agentic AI represents a shift from individual AI Agents to collaborative systems that can tackle complex tasks through multi-agent cooperation [24][27]. - The key difference between AI Agents and Agentic AI lies in the introduction of system-level intelligence, enabling broader autonomy and the management of multi-step tasks [27][29]. - Agentic AI systems utilize a coordination layer and shared memory to enhance collaboration and task management among multiple agents [33][36]. Group 4: Applications and Use Cases - The article outlines various applications of Agentic AI, including automated fund application writing, collaborative agricultural harvesting, and clinical decision support in healthcare [37][43]. - In these scenarios, Agentic AI systems demonstrate their ability to manage complex tasks efficiently through specialized agents working in unison [38][43]. Group 5: Challenges and Future Directions - The article identifies key challenges facing AI Agents and Agentic AI, including causal reasoning deficits, coordination bottlenecks, and the need for improved interpretability [48][50]. - Proposed solutions include enhancing retrieval-augmented generation (RAG), implementing causal modeling, and establishing governance frameworks to address ethical concerns [52][53]. - Future development paths for AI Agents and Agentic AI focus on scaling multi-agent collaboration, domain customization, and evolving into human collaborative partners [56][59].

Core Viewpoint - The article discusses the evolution and differentiation between Chat and Agent in the context of artificial intelligence, emphasizing the shift from mere conversational capabilities to actionable intelligence that can perform tasks autonomously [1][2][3]. Group 1: Chat vs. Agent - Chat refers to systems focused on information processing and language communication, exemplified by ChatGPT, which provides coherent responses but does not execute tasks [1]. - Agent represents a more advanced form of AI that can think, make decisions, and perform specific tasks, thus emphasizing action over mere conversation [2][3]. Group 2: Evolution of AI Applications - The development of smart speakers, starting from basic functionalities to becoming central hubs in smart home ecosystems, illustrates the potential for AI to expand its capabilities and influence daily life [4][5]. - The transition from simple AI assistants to AI digital employees that can both converse and execute tasks marks a significant evolution in AI technology [5][6]. Group 3: AI Agent Development Paradigm - The emergence of AI Agents signifies a profound change in software development, where traditional programming paradigms are challenged by the need for AI to learn and adapt autonomously [7]. - AI Agents are structured around four key modules: Memory, Tools, Planning, and Action, which facilitate their operational capabilities [7]. Group 4: Learning Paths for AI Agents - Current learning paths for AI Agents are primarily divided into two routes: one based on OpenAI technology and the other on open-source technology, encouraging developers to explore both avenues [9]. - The rapid development of AI Agents post the explosion of large models has led to a surge in various projects and applications [9]. Group 5: Notable AI Agent Projects - AutoGPT allows users to break down goals into tasks and execute them through various methods, showcasing the practical application of AI Agents [12]. - JARVIS is a model selection agent that decomposes user requests into subtasks and utilizes expert models to execute them, demonstrating multi-modal task execution capabilities [13][15]. - MetaGPT mimics traditional software company structures, assigning roles to agents for collaborative task execution, thus enhancing the development process [16]. Group 6: Community and Learning Resources - A community of nearly 4,000 members and over 300 companies in the autonomous driving sector provides a platform for knowledge sharing and collaboration on various AI technologies [19]. - The article highlights numerous learning paths and resources available for individuals interested in autonomous driving technologies and AI applications [21].

Investment Rating - The report does not explicitly provide an investment rating for the industry discussed. Core Insights - The "intelligent agent economy" is emerging as a significant topic, indicating a shift from AI as mere task executors to economic participants with identities and intentions, marking a new phase of human-machine symbiosis [3][9]. - AI intelligent agents are evolving to possess decision-making, execution, and collaboration capabilities, allowing them to autonomously plan, make decisions, and work together, thus moving away from human control [4][10]. - The development of intelligent agents will lead to a new work distribution logic, where AI can hire other AIs to complete tasks, creating a new economic network and challenging traditional business processes [6][12]. Summary by Sections Event Overview - At the Sequoia AI Summit in 2025, the concept of "intelligent agents" transitioning into economic behavior subjects was a focal point, highlighting their evolving roles in economic activities [3][9]. Commentary on AI Evolution - AI is transitioning from being a functional tool to an economic participant, gaining capabilities such as identity and intention expression, which allows for more autonomous operation in economic contexts [4][10]. Characteristics of Intelligent Agents - The core features of AI intelligent agents include their ability to make decisions, execute tasks, and collaborate with other agents, which enhances their functionality beyond traditional software [5][11]. New Economic Ecosystem - The intelligent agent economy is expected to accelerate AI commercial applications and restructure enterprise operations, moving from human-centric management to AI-driven task execution networks [6][13].

Core Insights - The emergence of the MCP (Model Context Protocol) is reshaping the AI industry, promoting a more egalitarian approach to technology and focusing on the effectiveness of AI products rather than the underlying models [1][3][10] - The global AI Agent market is projected to grow significantly, from $5.29 billion in 2024 to $216.8 billion by 2035, with a compound annual growth rate (CAGR) of 40.15% [3][10] - Major tech companies are increasingly adopting the MCP protocol, which aims to standardize interactions between AI models and external tools, akin to foundational internet protocols like HTTP [5][9] Industry Dynamics - The AI industry is experiencing a shift from traditional applications to AI Agents and terminal devices, driven by advancements in technologies such as natural language processing and machine learning [10] - The MCP protocol is seen as a solution to the complexities faced by developers in integrating various tools and models, highlighting a clear market demand for standardized protocols [8][9] - Companies like OpenAI, Tencent, and Alibaba are actively supporting the MCP protocol, indicating a collective movement towards a unified framework in the AI ecosystem [6][7][5] Competitive Landscape - The competition between MCP and Google's A2A (Agent2Agent) protocol illustrates the ongoing struggle for dominance in the AI Agent space, with both protocols seeking developer and enterprise support [7][9] - The industry is still in its early stages, with ongoing optimization of the MCP protocol and a focus on addressing the challenges of model consistency and interoperability [10][11] - The potential for collaboration between different protocols exists, particularly given the investment relationships among key players like Google and Anthropic [7][9] Future Outlook - The development of AI Agents is expected to lower the barriers for consumers in using software and smart hardware, with a focus on enhancing user experience through intuitive interactions [11] - The evolution of the MCP protocol is anticipated to address critical issues such as authentication and discovery mechanisms, which are essential for commercial applications [12] - As the market matures, the demand for effective applications rather than mere traffic aggregation will drive the future of the MCP marketplace [12]