Core Insights - The future of AI is characterized by "harness engineering," which emphasizes the integration of models, tools, and systems rather than focusing solely on individual models [16][19][21] - Open models are becoming increasingly significant, potentially forming the largest model group across various industries and applications [5][6] - The discussion highlights a shift from viewing AI as merely models to understanding it as a complex system that includes agents, orchestration, and governance [10][12][26] Group 1: Open Models and System Integration - Huang Renxun emphasizes that open models collectively represent the second-largest model group globally, with the potential to become the largest in various applications [5][6] - The conversation shifts from a binary view of open vs. closed models to a more nuanced understanding of how models, tools, and governance create a new system [6][10] - The emergence of a third category of companies that utilize the best model APIs while developing their own tools and agents indicates a more complex software stack [11][12] Group 2: The Role of Agents - Agents are evolving from simple models to complex systems capable of handling multi-step tasks and integrating various resources [36][40] - The concept of "agentic systems" is introduced, where agents can continuously process tasks and maintain state over time, moving beyond traditional models [36][40] - OpenClaw is highlighted as a significant project that exemplifies the capabilities of agentic systems, showcasing a new paradigm in computing [38][39] Group 3: Governance and Trust in AI - The discussion emphasizes that the real challenge for enterprises is not whether agents can perform tasks, but how to govern and manage them effectively [52][56] - Trust in AI systems is crucial, with open models being preferred for their transparency and verifiability, which helps build confidence in their deployment [56][67] - The need for a governance framework that addresses data access, action capabilities, and accountability is underscored as organizations begin to integrate AI into their operations [52][56] Group 4: The Importance of Open Models - Open models are seen as essential for customization, control, and cost-sharing in AI development, allowing organizations to tailor solutions to their specific needs [66][68] - The potential for open models to facilitate the creation of specialized digital twins in various fields, such as healthcare, is discussed [68][70] - The conversation highlights the need for open infrastructure to support the ongoing development and deployment of open models, ensuring they remain viable in the long term [72][73] Group 5: Future Directions and Industry Impact - The integration of AI into various sectors, including coding, healthcare, and robotics, is expected to accelerate as agents become more capable and reliable [62][64] - The discussion points to a broader trend where AI is not just about creating powerful models but about developing systems that can operate effectively in real-world environments [88][89] - The emergence of AI factories or foundries is anticipated, enabling companies to access necessary computational resources without needing to own them outright [83][84]

1948 年，Norbert Wiener 将这种模式命名为控制论（cybernetics）。 因此，真正值得追问的问题或许不是"AI 会不会取代程序员"，而是：当反馈回路终于能够在"架构 决策"这一层闭合时，工程师需要做什么，才能让这套机制真正运转起来？ 作者：George Zhang（OpenClaw 维护者 ） 本文是 George Zhang 对 Harness engineering 的解读，原文发布于他的 X。 今年 2 月，OpenAI 发布了一篇文章 Harness engineering: leveraging Codex in an agent-first world ， 描述了一种新的工作方式：工程师不再直接编写代码，而是设计环境、制定规则，让 agent 在其中 完成编码。 这篇文章很快在技术圈引发了广泛讨论。有人认为这是软件工程的终结，也有人觉得不过是新的炒 作。事实上，围绕 AI coding 的叙事一直在演化：从最早的 prompt engineering，到 context engineering，再到如今的 harness engineering，工程师的关注点逐渐从"如何与 ...