From 06595f9e0c6abc21956be0058d9efc5c0e362867 Mon Sep 17 00:00:00 2001 From: BlueRose <378100977@qq.com> Date: Thu, 19 Mar 2026 18:08:22 +0800 Subject: [PATCH] vault backup: 2026-03-19 18:08:22 --- .obsidian/app.json | 2 +- 07-Other/AI/AI Agent/WY/AI全生命周期构思.md | 60 +++++++++++++++++++ .../AI/AI Agent/WY/主Agent与从Agent.canvas | 44 ++++++++++++++ 07-Other/AI/AI Agent/WY/未命名.md | 20 ------- 4 files changed, 105 insertions(+), 21 deletions(-) create mode 100644 07-Other/AI/AI Agent/WY/AI全生命周期构思.md create mode 100644 07-Other/AI/AI Agent/WY/主Agent与从Agent.canvas delete mode 100644 07-Other/AI/AI Agent/WY/未命名.md diff --git a/.obsidian/app.json b/.obsidian/app.json index 0cd8116..900b5fb 100644 --- a/.obsidian/app.json +++ b/.obsidian/app.json @@ -4,7 +4,7 @@ "promptDelete": false, "pdfExportSettings": { "includeName": true, - "pageSize": "A3", + "pageSize": "A4", "landscape": false, "margin": "0", "downscalePercent": 87 diff --git a/07-Other/AI/AI Agent/WY/AI全生命周期构思.md b/07-Other/AI/AI Agent/WY/AI全生命周期构思.md new file mode 100644 index 0000000..805cb50 --- /dev/null +++ b/07-Other/AI/AI Agent/WY/AI全生命周期构思.md @@ -0,0 +1,60 @@ +# 前言 +现阶段其他部门都关注于自己工作领域，而忽视了项目全生命周期流程，一些问题往往会在领域交接时产生，之后的章节会逐步展开。在这之前我说这几点： +- 主Agent与从Agent +- 项目知识管理 +- 文档&提示词的质量 +- 记忆系统管理 与 Skill + +## OpenClaw与多Agent +现阶段因为各种限制不会出现全能性AI，所以建议使用主Agent与多个从Agent的方案。但对于公司而言，All In One的方案并不是最好选择，除了考虑避免All In Boom，还有就有就是记忆、上下文、方便迭代方面的考量。 + +>***另据我所知原生提供Node远程控制方案并且可以自己部署节点的只有OpenClaw。*** 除此之外全球的开源力量使得迭代速度指数级上升，似乎有可能成为新的linux，成为AI时代的agent 中继 Bridge。 + +基于以上几点我选择OpenClaw，以下是我个人建议架构： +![[主Agent与从Agent.canvas]] +项目组主要产出文档以及代码，使用主Agent的使用目的主要集中精炼文档、记忆、Skills、沟通其他Agent。 +其他点： +1. Agent之间的沟通存在token消耗。目前已经我已经找到解决方案准备测试。 +2. 一些相关信息也建议汇总到主Agent中进行汇总，但需要注意使用分层结构，避免出现降智问题。 + +## 知识管理 +现阶段Markdown（之后简称MD） 因为高可读性、高信息密度、纯文本成为AI Agent的文档首选，。我个人推荐Obsidian作为MD的编辑与查看工具，且已有现成的MCP与CLI工具。（还有看板、数据表、画板、导图、流程图等功能）另外个人推荐使用使用git+oss的方式来做版本管理与协作，想简单可以使用SMB。 + +推荐使用多层结构进行管理，避免Context爆炸。这个技巧也可以使用在Skill中。结构参考： +- AI策划助手_使用说明.md +- 表结构 + - 道具表说明书.md + - 道具表说明书_耕种畜牧.md + - 道具表说明书_加工品消耗品.md + - 道具表说明书_设施与建筑.md +- 配表流程 + - skill_开单说明.md + - skill_配表.md + +## 记忆系统 +目前已知大致有下面3个流派： +- 结构化知识图谱派：Evolver +- 向量检索派：MemorySearch +- 生成式摘要派：self-improving-agent + +实际上记忆系统都是混合方案。面向多人的企业级方案才是焦点。 + +### “精耕细作” 式管理记忆 +多人使用场景下，往往会有堆积大量 “噪音”以及错误记忆，尽管有一些工具可以自动优化，但依然建议人工介入，让记忆（经验）更快“收敛”。 + +## 文档&提示词的质量 +某专家策划曾提出以下几个问题： +1. 能力不同的人，编写的文档，是否会对AI的理解与执行产生较大影响？ +2. 程序需要多加限制还是少加限制？ +3. 什么样的文档适合AI来阅读？ + +我个人认为逻辑清晰认知高的人编写的文档与提示词，AI更容易理解意图并且能够更好的执行； +程序应该关注每个功能（维度从function => Module)的输入与输出是否正确，所以应该让AI自己发挥除了硬杠杠与主要方向。 +AI写的文档往往比人写的更加简练，更高复用性，所以更适合AI阅读。 + +--- +# 策划 +TODO + +# 程序 +TODO diff --git a/07-Other/AI/AI 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-现阶段其他部门都关注于自己工作领域，而忽视了项目全生命周期流程，一些问题往往会在领域交接时产生。 - -## 知识管理 -现阶段Markdown（之后简称MD） 是AI Agent的首选，我个人推荐Obsidian作为MD的编辑与查看工具，且已支持 -知识管理方法 - -## 文档质量 - -1. 如何评价文档质量？ -2. 谁来写这个文档？ - -## 记忆系统 -### 三层记忆系统 - - -# 程序方面 - - -