A Thought Leader's Perspective: Power and Responsibility | 思想领袖的洞见：权力与责任

Of all the technology cycles I've witnessed over the past four decades—from the birth of the personal computer and the web, to the revolutions in mobile and cloud—none has felt quite like this one. For years, the discourse around Artificial Intelligence was a familiar rhythm of hype and disillusionment, the so-called "AI summers" followed by long, cold winters. But this time, something is different. The conversation has palpably shifted. If the last eighteen months were about the engine -the breathtaking, almost vertical ascent of Large Language Models (LLMs)- the next era will be about the car we build around it. It will be about the frameworks that harness this raw power, transforming it from a generator of plausible text into a true agent of action.

在过去四十年我所见证的所有技术浪潮中——从个人电脑和互联网的诞生，到移动和云计算的革命——没有一次像今天这样。多年以来，围绕人工智能的讨论始终遵循着一种熟悉的节奏：始于大肆宣传，终于幻想破灭，所谓「AI 之夏」之后，总是伴随着漫长而寒冷的冬天。但这一次，情况有所不同，风向发生了切实的转变。如果说过去的十八个月是关于「引擎」的故事——即大语言模型那惊人的、近乎垂直的飞跃——那么下一个时代将是关于我们如何围绕它造出一辆「汽车」。这个时代，将关乎我们如何构建框架来驾驭这股原始的力量，把它从能生成看似合理文本的工具，打造成真正能付诸行动的智能体。

I admit, I began as a skeptic. Plausibility, I've found, is often inversely proportional to one's own knowledge of a subject. Early models, for all their fluency, felt like they were operating with a kind of impostor syndrome, optimized for credibility over correctness. But then came the inflection point, a step-change brought about by a new class of "reasoning" models. Suddenly, we weren't just conversing with a statistical machine that predicted the next word in a sequence; we were getting a peek into a nascent form of cognition.

坦白说，我起初是怀疑的。我发现，一件事物的「貌似可信度」，往往与我们对该主题的了解程度成反比。早期的模型，尽管语言流畅，却仿佛患上了「冒名顶替综合征」，它们被优化的目标是追求可信度，而非正确性。然而，转折点随之而来——推理模型的出现，实现了一次质的飞跃。那一刻，我们对话的对象不再仅仅是那个预测词语的统计机器；我们所窥见的，是一种正在萌芽的全新认知。

The first time I experimented with one of the new agentic coding tools, I felt that familiar spark of magic. I tasked it with a personal project I'd never found the time for: migrating a charity website from a simple web builder to a proper, modern CI/CD environment. For the next twenty minutes, it went to work, asking clarifying questions, requesting credentials, and providing status updates. It felt less like using a tool and more like collaborating with a junior developer. When it presented me with a fully deployable package, complete with impeccable documentation and unit tests, I was floored.

当我第一次试用一款新型的智能体编程工具时，我感受到了那种久违的、如魔法般的火花。我让它去做一个一直无暇推进的个人项目：把一个慈善网站从简易的网页构建器，迁移到一个规范、现代的 CI/CD 环境中。在接下来的二十分钟里，它开始工作，不断提出澄清问题，请求授权凭证，并提供进度更新。这感觉不像是在使用一个工具，更像是在与一位初级开发人员协作。当它最终向我提交一个带有无可挑剔的文档和单元测试、可完全部署的软件包时，我被彻底震撼了。

Of course, it wasn't perfect. It made mistakes. It got stuck. It required my supervision and, crucially, my judgment to steer it back on course. The experience drove home a lesson I've learned the hard way over a long career: you cannot afford to trust blindly. Yet, the process was fascinating. Peeking into its "chain of thought" was like watching a mind at work—messy, non-linear, full of starts, stops, and self-corrections, not unlike our own human reasoning. It wasn't a straight line; it was a random walk toward a solution. Here was the kernel of something new: not just an intelligence that could generate content, but one that could generate a plan.

当然，它并非完美。它会犯错，会卡住。它需要我的监督，以及至关重要的——我的判断力来引导它重回正轨。这次经历让我深刻地体会到了我在漫长的职业生涯中历经坎坷才学到的一个教训：你永远不能盲目信任。然而，这个过程本身却极其迷人。窥视它的「思维链」，宛若观看一颗大脑的运作——杂乱、非线性，充满开始、停顿与自我修正，这与我们人类的推理别无二致。那不是一条直线，而是一场通往解决方案的随机游走。在这里，我看到了新事物的雏形：一种不仅能生成内容，更能制定计划的智能。

This is the promise of agentic frameworks. It's the difference between a static subway map and a dynamic GPS that reroutes you in real-time. A classic rules-based automaton follows a fixed path; when it encounters an unexpected obstacle, it breaks. An AI agent, powered by a reasoning model, has the potential to observe, adapt, and find another way. It possesses a form of digital common sense that allows it to navigate the countless edge cases of reality. It represents a shift from simply telling a computer what to do, to explaining why we need something done and trusting it to figure out the how.

这便是智能体框架所带来的希望。它就像一张静态的地铁线路图与一个能为你实时重新规划路线的动态 GPS 之间的区别。一个经典的、基于规则的自动程序遵循固定的路径，当遇到意外障碍时，它就会崩溃。而一个由推理模型驱动的 AI 智能体，则有潜力去观察、适应并找到另一条路。它拥有一种数字化的常识，使其能够应对现实世界中无数的边缘案例。这代表着一种转变：我们不再是简单地告诉计算机「做什么」，而是向它解释「为什么需要做某件事」，并相信它能自己找出「如何做」。

As exhilarating as this new frontier is, it brings a profound sense of responsibility, particularly from my vantage point as the CIO of a global financial institution. The stakes are immeasurably high. An agent that makes a mistake while creating a recipe for a "Chicken Salmon Fusion Pie" is a fun anecdote. An agent that makes a mistake while executing a trade, managing risk, or handling client data is a real problem. I've read the disclaimers and the cautionary tales: the web automation agent that, after failing a login, decided to email a member of parliament to complain about login walls. It's a darkly humorous reminder that we are dealing with a technology we don't fully understand.

尽管这个新领域令人振奋，但它也带来了一种深远的责任感，尤其从我作为一家全球金融机构首席信息官的视角来看更是如此。这里的风险之高，不可估量。一个智能体在为「鸡肉三文鱼融合派」创建菜谱时犯了错，不过是个有趣的轶事。但如果一个智能体在执行交易、管理风险或处理客户数据时犯了错，那就是一个实实在在的大问题。我读过那些免责声明和警示故事：一个网络自动化智能体在登录失败后，竟然决定给一位国会议员发邮件抱怨登录墙。这是一个黑色幽默般的提醒：我们正在打交道的，是一项我们尚未完全理解的技术。

This is where craft, culture, and a relentless focus on our principles become our essential guide. Our Engineering Tenets are not just words on a page; they are our compass. We must Build with Purpose, ensuring that every agent we design starts from a clear understanding of the client problem we are solving. We must Look Around Corners, anticipating failure modes and designing systems that are resilient by design. And above all, we must Inspire Trust, by being transparent about our methods and accountable for our outcomes.

正是在这里，专业精神、企业文化以及对原则的执着追求，成为了我们至关重要的指南。我们的工程信条不是纸上的口号，而是我们的指南针。我们必须为使命而构建：确保我们设计的每一个智能体都始于对我们正在解决的客户问题的清晰理解。我们必须洞见未来，防患未然：预见各种失败模式，并设计出具有内在韧性的系统。最重要的是，我们必须启迪信任，不负所托：对我们的方法保持透明，对我们的结果负责。

In an agentic world, these tenets take on new urgency. The hard truth is that you cannot simply overlay these powerful new tools onto messy, inconsistent systems and expect good results. Messy systems plus agents are a recipe for disaster. An AI trained on "garbage" data doesn't just produce garbage-out; it produces plausible, confident garbage that can poison an entire process. Therefore, our first and most critical task is to prepare the ground. We must invest in clean data, consistent metadata, and well-defined APIs. We have to build the modern "interstate system" that allows these agents to operate safely and at high velocity. It is the hard, foundational work of building a programmable enterprise, an "enterprise as software," where our processes are as well-architected as our code.

在一个智能体化的世界里，这些信条被赋予了新的紧迫性。一个残酷的现实是，你不可能简单地将这些强大的新工具叠加在混乱、不一致的系统之上，并期望得到好的结果。混乱的系统加上智能体，只会酿成灾难。一个用垃圾数据训练出来的 AI，不仅会产生垃圾结果，它还会产生貌似可信、充满自信的垃圾，足以毒化整个流程。因此，我们首要且最关键的任务，是打好基础。我们必须投资于干净的数据、一致的元数据和定义良好的 API。我们必须建立起现代化的“州际高速公路系统”，让这些智能体能够安全、高速地运行。这是一项艰巨的基础性工作，其目的就是构建一个可编程的企业——即实现“企业即软件”的理念，从而让我们的业务流程也能像代码一样，拥有精良的架构。

Ultimately, this journey is not about replacing human ingenuity, but about augmenting it. It demands a new set of skills from all of us: the ability to explain a task with clarity, the wisdom to delegate, and the diligence to verify the quality of the output. It requires us to be humble, to acknowledge what we don't know, and to never stop learning. The pages that follow in this book offer a technical map for building these new frameworks. My hope is that you will use them not just to build what is possible, but to build what is right, what is robust, and what is responsible.

归根结底，这段旅程并非要取代人类的智慧，而是要增强它。它要求我们所有人掌握一套新的技能：清晰解释任务的能力，明智授权的智慧，以及验证产出质量的勤勉。它要求我们保持谦逊，承认自己的未知，并永不停止学习。本书接下来的篇章给出搭建这些新框架的技术地图。愿各位不止于把可能之事做成，更能把正确、可靠且负责任的事做好。

The world is asking every engineer to step up. I am confident we are ready for the challenge.

这个世界正在召唤每一位工程师挺身而出。我相信，我们已经准备好迎接挑战。

Enjoy the journey.

愿你享受这段旅程。

Marco Argenti, CIO, Goldman Sachs

Marco Argenti，高盛首席信息官（CIO）

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