一个从零设计的智能体：先预测再行动，递归自我改进
Santiago 在推上介绍了一个新 agent 架构：用 world model 先预测决策对业务的影响再执行，还能根据结果递归改进。相比常见的先干后改的 agent，多了事前推演这一步。做 agent 开发的人可以看看思路。
一条推文介绍了一个面向数字业务日常运营而设计的智能体。它的核心新意在于两点：一是内置一个 world model，在真正执行动作之前，先预测不同决策会对业务造成什么影响；二是把此前决策的结果反馈回来更新策略，实现递归式自我改进。
从机制上看，这把 agent 的执行循环从“感知—行动”扩展成了“预测—行动—复盘—更新”，相当于把决策影响评估和经验回写做进了 agent 架构本身，而不是依赖外部人工调优。推文没有披露模型名、参数规模、基准成绩或价格等具体信息，属于概念层面的介绍。
对于关注 agent 框架方向的开发者来说，这个思路代表了当前的一个讨论热点：让智能体具备事前推演能力，而不只是事后纠错。具体实现细节还需等待作者的后续说明。
This is a new agent designed from the ground up to run the day-to-day work of a digital business.
Here is what's new about this:
1. It uses a world model to forecast how different decisions could affect the business before taking action.
2. It uses the results of previous decisions to update its approach, recursively self-improving.
This report is categorized under AITOP's "Products" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include 智能体, world model, Agent 架构.
Source attribution: curated from Ate-a-Pi (@svpino).
