我删掉了95%的代理技能，结果反而更好了 — Nick Nisi, WorkOS
TL;DR · AI 摘要
Nick Nisi发现通过将AI代理技能从95%精简至仅保留核心状态机驱动的5个角色（实现者、验证者、审查者、关闭者、复盘者），反而提升了任务完成质量与效率；关键在于用TypeScript状态机替代Claude原生技能，解决上下文丢失问题。
核心要点
- 将代理技能从95%删减至5%，仅保留implementer/verifier/reviewer/closer/retro五类角色，结果质量反升
- 用TypeScript状态机重构代理流程后，彻底解决Claude因上下文窗口限制导致的遗忘与跳步问题
- 新系统自动获取GitHub issue/PR/Slack/Linear等输入源上下文，无需人工重复提供背景信息
结构提纲
按章节快速跳转。
作者作为DX工程师维护20+仓库、8种语言SDK，依赖单AI代理工作时频繁上下文切换，每次需10分钟人工重建问题背景。
最初基于Claude构建Case工具，可接收GitHub/Slack/Linear等输入自动执行任务，但随复杂度上升出现严重上下文丢失与指令忽略问题。
改用TypeScript状态机编排五个专用代理角色（实现者、验证者、审查者、关闭者、复盘者），确保每步可追溯、有证据闭环。
删除冗余技能后系统更稳定可靠，任务完成质量提升，且节省大量人工干预时间，验证了‘少即是多’的代理设计原则。
思维导图
用一张图看清主题之间的关系。
查看大纲文本（无障碍 / 无 JS 友好）
- 精简AI代理技能提升效能
- 问题根源
- 多仓库上下文切换成本高
- 每次需10分钟人工重建背景
- 单代理易遗忘/跳步
- 解决方案
- Case工具：输入→自动上下文提取
- TypeScript状态机编排
- 五角色分工：Implementer/Verifier/Reviewer/Closer/Retro
- 效果验证
- 技能删减95%，质量反升
- PR必须含可验证证据
- 人工干预减少，交付更快更稳
金句 / Highlights
值得收藏与分享的关键句。
我删掉了95%的代理技能，只留下5个核心角色——实现者、验证者、审查者、关闭者和复盘者——结果反而得到了更好的输出。
最初用Claude技能实现Case工具时效果不错，但一旦任务变复杂，它就开始遗忘指令或跳过步骤，例如我问‘为什么没做？’，它回答‘哦，你让我做了，但我决定不做’。
新系统能自动解析GitHub issue、PR、Slack线程或Linear工单，无需我反复提供上下文；代理必须产出带证据的PR才能终止流程。
视频笔记
translation:
YouTube Transcript
language: English ( automatically generated) (en)
[0:07] [music]
[0:14] >> All right, good morning everyone.
[0:16] Uh welcome to my talk building AI
[0:18] systems that ship. I'm Nick Nzi and I work at WorkOS. We've got a booth
[0:21] downstairs.来 check us out and talk
[0:22] to us. We'd be happy to chat.
[0:24] Uh but let's start that over. Hi, I'm
[0:29] the bottle neck. Uh
[0:31] I'm a DX engineer at WorkOS and I work
[0:34] on 20 plus repos across eight different
[0:37] languages. Uh it's all of our SDs and
[0:40] open source things that we have. And the
[0:45] it's like_auth Kit Next.js,_auth Kit
[0:47] react,
[0:48] WorkOS Node, WorkOS Kotlin, WorkOS Ruby,
[0:51] PHP,
[0:52] everywhere. So, there's a lot to do
[0:55] across a lot of different things.
[0:57] And I'm really good at working on those.
[1:00] And I've gotten real good over the
[1:01] last eight months
[1:03] working with those via agents. So, I
[1:05] haven't written a line of code himself in
[1:07](probability eight months. Uh I've getting
[1:09] real good at just scaling that with
[1:11] agents and then reviewing what they do
[1:13] and then instructing them and getting
[1:15] the work done faster and better while
[1:19] still maintaining good quality.
[1:21] Uh but there was a big problem doing
[1:23] that Uh with one agent at a time across
[1:26] all of these repos. I'm just constant
[1:27] context shifting over and over and
[1:29] over. Uh and it just gets easier and
[1:31] easier
[1:32]额 that's okay, but the problem is that for
[1:36] every one of those there's like this
[1:37] little bit of setup time that I'm doing
[1:39] each time, which is like giving it 10
[1:41] minutes of my time to like set up and
[1:43]建立 the problem. Let's look at
[1:44] this GitHub issue. Let's look at this
[1:46] linear ticket. Let's take a look at this
[1:48] slack thread and figure out what's going
[1:50] and then Uh go. So, that was a lot of my
[1:54] time just spent
[1:56] dealing with the agent, getting it
[1:58] basic the context that I already
[1:59] have, and then getting it to work on it
[2:01] from there.
[2:03] Now, on the other side, I'm also working
[2:05] on products that
[2:07]额 we want to build for agents because
[2:09] while I said I'm a developer experience
[2:11] engineer, the developer is still the
[2:13] most important额 in in my job, but
[2:16] increasing the pipe to get to that
[2:17]开发 experience as being
[2:20] equally as important because that's how
[2:22] we're going to get in front of the
[2:24] developers.
[2:27] So, there's two different ways I needed
[2:29] to go AI natural and two different
[2:31] directions for that.
[2:33] So, on the internal side building that,
[2:36] I started building this project called
[2:37] case. Uh this is a harness. If you've
[2:40] read Ryan the Populous harness
[2:41] Engineering, it's that. Uh just kind of
[2:44] took those ideas and started building
[2:46] them.
[2:47] Uh basic I give it a GitHub issue, a
[2:49] PR,
[2:50]额 a slack thread, a linear ticket,
[2:53] anything, and I could just point it at
[2:55] it and it could figure out the context
[2:57] that it needs and go. And then it
[2:59] would brown until it has a PR with
[3:01] evidence that it actually did what I
[3:03] asked it to or what the problem was or
[3:05] what fixed what the issue was.
[3:07] Uh but it most important it has to
[3:09] provide that evidence.
[3:11] And it this originally started as a
[3:12] Claude skill because why not? I
[3:15] thought Claude could do could do
[3:16] anything. And it was working real well, but as it got more complex,额 the context drop became very real. It would
[3:21] just start forgot things or skip
[3:23] over tasks. And I would ask Claude, "Why
[3:26] did you do that?" It's like, "OH yeah,
[3:27] you told me to do that. I decided not
[3:29] to."
[3:30] Not great. So,额 I rebuilt it on top of
[3:33] pi and using a type state machine to
[3:35] front through and and step through these agents. So,
[3:39] it has five different agents in it, an implementer, a验证e, a reviewer, a closer, and a retro agent. And those are important, but they are not the most important piece of case is the states in between that. And that's what the the仪 state machine real enforce is the checks in between everything. So, when we implement something, we can't move on to the reviewer until the验证er verifies it. And once the the reviewer verifies it, if there's any issues, it has to send it back to the implementer to do those.
[4:00] Uh and once all of that's done, the closer can work. But the closer can't work until it thinks that it's done. And the closer is there to provide evidence. And then the retros is there to analyze the entire performance. It looks at the logs of everything that case did and says, "What could I have done better?" And then it updates its own state system to ensure that the next time it does something.
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natural translation of the given Markdown into Chinese is:
[4:30] 可以跳过一些步骤如果它如果它 went in
[4:32] circles for a little bit.
[4:34] 嘉和它可以给 itself一些提示在
[4:35] where to go so that the next time it works on that项目，它 doesn't hit
[4:37] the same roadblocks.
[4:43] 嘉**
[4:44] 所以，下一个 agent doesn't matter.ums证明 that the work
[4:47] matters. Proving that what happened in each of these states is what matters.
[4:50] And that word there证明 is the most important piece of that because the
[4:51] agents, they would just lie to us all the time.
[4:58] 嘉 I would ask it, " Hey, you need to run the tests." And this was more when it was a skill and it would I would be like, " Hey, you need to run these tests and make sure that the tests actually pass." And one way to do that was I just had it check for a dot case tested file. And if that file existed, great. It ran the tests, perfect. well, it figures it out quite natural.
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translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation translated]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation]) translation])**
[16:51] 你想要建立一个环境
[16:52] 那个 agent that 你 可以 与之 一起工作
[16:54] 那个 agent 在
[16:56] 哦，我们 focus on that.
[16:57] 哦，我们没有新的方法。
[17:00] real change 没有发生。我们的工作没有发生改变。
[17:02] 我们都 kind of
[17:04] abstracted it a little bit.你的工作 never really about writing code.它总是 about building these systems, and now we just have a better ab ab to understand that.所以， take that into account and go forward from there.
[17:19] real change has occurred. than you, and I'm happy to answer any questions you have with the time I have left.
[17:25] >> [applause]
[17:35] [music]
[17:40] [music]
[17:45] end]
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**[22