话语场

演讲 · AI Engineer · 2026-09-26 · 17 分钟 · EN

一个会自我改进的智能体是怎么造的

How We Built an Agent That Improves Itself — Zubin Aysola, Weights & Biases

观看原片 ↗境内需要国际网络

核心内容

Zubin Aysola 介绍一个自我改进智能体的构建过程,关注如何把反馈和迭代放进智能体系统。

介绍依据 · 发布标题 · 完整内容待核读

编辑导览,依据原始来源 ↗,整理于 2026-10-06。 介绍只说明标题中的讨论议题,不据此判断讲者结论。

自我改进智能体的核心闭环在于抹平研究与生产环境的差异:通过生产追踪自动抓取失效样本,并在字节级一致的环境中运行离线评估与评分,使智能体能够在观测中自我迭代。

原话

2逐字核对过;核对了说话人和位置
Um so I think the exciting part here is that both using the logging framework from weave as well as just in the way that we've designed the system our research and production code are exactly the same. And in fact, there's like a 4-hour sync job that happens between production to
自动字幕 · 已核对Zubin Aysola▶ 00:05:35
instead of going back to my cloud code and writing offline benchmarks and trying to think about what I want the agent to do, I'm just going to live in this platform instead and hop between my production tracing project where I'm looking at traces roll in as they come through over time where AR is, you know, building itself
自动字幕 · 已核对Zubin Aysola▶ 00:14:13

主张

1转述不加引号;附原文依据和位置,核对范围见语境与收据

离线评估必须在与线上生产字节级完全一致的环境中执行与打分,而评分体系的稳健性设计是衡量自我改进成果的核心工作。

相关