想让智能体越用越聪明吗？这篇文章手把手教你从用户操作和自身轨迹中学习，还有三种优化方式和数据安全建议，非常实用。
本文介绍构建自学习智能体的方法。关键在于从两种来源学习：agent轨迹显示智能体行为和故障点，浏览器活动记录用户如何修正结果。有三种应用新知识的方式：微调模型、更新harness、提供上下文信息。建议侧重程序记忆（存储工作流和规则）和情节记忆（存储具体事件），避免过度依赖语义记忆。学习范围应限定在每用户、每团队或每应用级别，防止用户间数据泄露。最终强调拥有学习数据是核心资产，并使用开放标准。
How you can build a moat with self-learning agents…
How you can build a moat with self-learning agents:
If you can build an agent that gets better every time people use it, you will be unstoppable.
Here is what you can do:
1. Learn from two sources, not one. Agent traces show what the agent did and where it broke. In-browser activity shows how users steered and fixed the results. Most products capture the first source and forget the second.
2. You have three options to apply new learnings: fine-tune your model, update the harness, or provide in-context information to the agent. Make sure you take advantage of all three.
3. When deciding what your agent should remember, lean on procedural memory (where you store workflows and rules) and episodic memory (where you store specific things that happened). Don't over-index on semantic memory, as it can go stale and become a liability.
4. Set boundaries around how far learning spreads (per-user, per-team, per-app). Be careful not to leak facts between different users' contexts.
5. You want to own the data your agent learns from. This data is your most valuable asset. Try to keep it in your infrastructure so it doesn't get stolen by Big Cloud.
6. Use open, framework-agnostic standards to capture and apply learnings.
Here is an article with more information: