RT艾玛：我真的很喜欢这件作品。这是一篇罕见的文章，从直观的角度解释了“自我进化的代理产品”实际上如何.
RT Emma: I really enjoyed this piece. It’s a rare article that explains, at an intuitive level, how “self-evolving agent products” actually work to...
Amjad MasadAI2026-07-09
RT Emma我真的很喜欢这件作品。这是一篇罕见的文章，从直观的角度解释了“自我进化的代理产品”当今的实际工作原理。我的要点是：系统需要通过控制上下文从真实用户交互中建立有效的数据反馈循环;通过创建准确的端到端行为评估来超越静态基准，这些评估可以成为代理迭代的自动优化循环;并使用模型自己的推理和编码能力来推动产品大规模改进（即自动聚集大量生产日志以识别系统故障，然后让人工智能编写补丁、运行测试并自行提交PR）。通过这样的机制，开发人员可以构建能够自我修复和持续改进的代理系统。希望有一天我也能像这样分享我的学习和观察。Amjad Masad：许多人都在问Replit如何进步如此之快-我们关闭了循环，代理正在自我改进。技术详情请参阅：
原文
RT EmmaI really enjoyed this piece. It’s a rare article that explains, at an intuitive level, how “self-evolving agent products” actually work today.My takeaway is this: a system needs to build an effective data feedback loop from real user interactions by controlling context; move beyond static benchmarks by creating accurate end-to-end behavior evals that can become automated optimization loops for agent iteration; and use the model’s own reasoning and coding capabilities to drive product improvements at scale (i.e. automatically cluster large volumes of production logs to identify systemic failures, then have AI write the patch, run tests, and submit the PR on its own.)Through mechanisms like these, developers can build agent systems that can self-repair and continuously improve.Hopefully one day I’ll be able to share my learning and observation like this one too.Amjad Masad: Many are asking how Replit is improving so rapidly—we closed the loop and the agent is self-improving. Technical details here: