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 ...

Deep Agents Overview - Deep Agents is an open-source agent harness incorporating planning, computer access, and sub-agent delegation tools, commonly found in agents like Manis and Cloud Code [1][46] - The harness is designed to be easily adaptable with custom prompts, tools, and sub-agents [2][47] Key Features and Tools - Deep Agents provides built-in tools such as planning, sub-agent delegation, and file system operations [6][7] - The built-in tools enable interaction with the file system, shell command execution, planning via to-dos, and task delegation [8] - Custom tools, instructions, and sub-agents can be added to Deep Agents to tailor it for specific use cases [6][47] Quick Start and Research Application - The Deep Agent quick start repo offers examples for different use cases, starting with research [2][5] - The research quick start includes tools like a search tool (using Tavi search API) and an optional "think" tool for auditing agent trajectory [12][13][14] - Task-specific instructions and sub-agents can be supplied to Deep Agents for any given use case [12] Agent Loop and Middleware - Deep Agents utilizes Langraph for orchestrating the agent loop, which involves the language model (LLM) calling tools in a loop [29] - Middleware serves as hooks within the agent loop, allowing for actions like summarization when context exceeds 170,000 tokens [30][32] - Middleware can provide tools to the agent, such as file system middleware, and perform actions like summarization and prompt caching [31][34] File System and State Management - By default, Deep Agents writes to an internal in-memory state object, but it supports different backends like a sandbox or local file system [37][38] - File reading and writing operations occur within the Langraph state object, enabling easy retrieval into the LLM's context window [40] Deployment and Visualization - Deep Agents can be run in a Jupyter notebook for interactive inspection or deployed as an application using Langraph [10][44] - A UI can be connected to the local Langraph server for visualizing generated files and agent interactions [3][45]

Check this out. I just asked an agent to answer one of the world's greatest debates. Is Messi or Ronaldo the greatest soccer player of all time.This isn't an easy question to answer, and it definitely requires a good amount of research. The agent automatically spawned two parallel sub agents to look into each of their achievements. This meant searching the web over a dozen times, compiling a comprehensive report with cited sources.To be extra thorough, the agent then critiqued its own report and plugged any ...