正版资源教学员如何使用 Claude Code 摆脱传统的手写测试脚本Agentic Automation — A Pytest Framework with Claude Code
《Agentic Automation: Skills, Subagents, Planning, and Test Automation》是 Josef Levy 的一门英文课程，2026 年 9 月发布，共 49 讲、3 小时 6 分。它教的不是编曲，而是如何用 Claude Code 摆脱手写
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《Agentic Automation: Skills, Subagents, Planning, and Test Automation》是 Josef Levy 的一门英文课程，2026 年 9 月发布，共 49 讲、3 小时 6 分。它教的不是编曲，而是如何用 Claude Code 摆脱手写测试脚本：从零编写六个各司其职的子智能体，再把它们串成一个能互相交接工作的自动化闭环。对制作人来说，它的位置在“工具链那一侧”：当你需要自己写脚本维护工作流时，课程里“把职责切窄、只给必要的工具”这套组织方式可以直接借用；如果你完全不碰代码，它和你的日常制作没有交集，跳过即可。
六个子智能体与“规划模式”的分工
课程的核心动作是把一个大而全的助手拆成六个角色，涵盖架构规划、代码编写、代码评审、失败原因调查等环节。每个子智能体只拿一份很窄的授权，以及完成这件事刚好够用的工具，避免一个智能体既想方案又改代码还给自己打分。串联之后形成的是工程闭环：上一环的产出交给下一环，失败原因调查的结果再回流到编写环节。
技术栈落在 Python、Pytest 和 Playwright 上。课程重点讲“规划模式”——先把思考和执行分开，让智能体在动手前把方案摊开，再进入实际改动。这个习惯对任何自动化流程都成立：先看清楚要做什么，再让它碰文件。
安全护栏与权限策略
课程花了相当篇幅讲如何在严格的安全护栏和权限策略下优化智能体。这是自动化能不能长期跑下去的前提——权限给多了，一次误操作就够你收拾半天；给少了，智能体寸步难行。课程面向的是有 Python 基础的中级开发者，完全没有编程经验的人跟起来会吃力，因为示例默认你能读懂代码结构和测试断言。
素材本身是 4K（3840×2160）的 MP4，AAC 44.1 kHz 双声道，3.3 GB，英语授课。分辨率高但体积不小，建议先在线看完再决定是否本地留存。三小时的体量适合分两三次看完，边看边在自己的环境里复现那六个子智能体的配置，比纯观看有效得多。
这门课程教学员如何使用Claude Code摆脱传统的手写测试脚本，转而构建一个由 AI 智能体（Agents）组成的“虚拟测试团队”。你将学习从零编写 6 个各司其职的专业子智能体（涵盖架构规划、代码编写、代码评审、失败原因调查等），并将它们串联成一个自动化、能互相交接工作的多智能体工程闭环。
在技术实现上，课程聚焦于Python、Pytest 和 Playwright框架，重点教授如何利用“规划模式”分离思考与执行，以及如何在严格的安全护栏与权限策略下优化智能体。这适合有一定 Python 基础、希望将前沿的 AI 智能体技术应用到软件测试自动化中的中级开发者。
Published 9/2026
Created by Josef Levy
MP4 | Video: h264, 3840×2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 49 Lectures ( 3h 6m ) | Size: 3.3 GB
Agentic Automation: Skills, Subagents, Planning, and Test Automation
What you’ll learn
⚡ Design and author purpose-built Claude Code subagents — giving each a narrow mandate and exactly the tools it needs
⚡ Separate thinking from doing with plan mode — produce a reviewable plan before writing code
⚡ Orchestrate a multi-agent engineering loop — plan → build → review → test — where a lean orchestrator routes work across agents
⚡ Turn requirements into a running Pytest + Playwright framework — mapping user stories and acceptance criteria into non-flaky automated tests
⚡ Work within enforceable guardrails and improve agents from real results — building inside allow/deny permission policies
Requirements
❗ Comfortable with Python, Pytest, and Git; some Playwright and Claude Code exposure helps
Description
Agentic Automation Course
Build a test-automation framework where the work is done by AI agents, not scripts you run by hand.
Most automation courses teach you to write scripts. This one teaches you to build ateam— a roster of purpose-built Claude Code subagents that plan, write, review, run, and debug tests by reasoning about the work, using their own tools, and handing off to each other in a real engineering loop.
You’ll author each specialist from scratch and wire it into a working pipeline
✅plan-architectresearches a task and returns a reviewable plan before a line of code is written
✅automation-writerturns user stories and acceptance criteria into non-flaky Pytest + Playwright tests
✅senior-code-reviewerreviews the diff read-only and opens the PR on approval
✅test-suite-plannerdecomposes a story into prioritised, independent suites as Jira sub-tasks
✅test-runnerexecutes each reviewed suite and posts a sanitized verdict
✅qa-failure-investigatortells a test bug from a product bug — byattempting the fix— and files evidence-backed defects
A leanorchestratorroutes work between them and carries context across the hand-offs. Because the heavy lifting stays inside each agent’s isolated context window, the orchestrator runs a long, multi-stage workflow without exhausting its own — in one measured build it held at~5%
context usedwhile ~250K tokens of work happened inside the subagents.
What makes it different
✅“Agentic” is literal.Each agent is an autonomous LLM worker with a narrow mandate, its own context, and exactly the tools it needs — toolsarethe capability boundary, so a read-only reviewer physically can’t mutate the repo.
✅Dogfooded throughout.Every agent is exercised on real work in the course’s own repo, driving a live Jira board and a real browser against a running app. Whatever breaks gets fed back into that agent’s prompt — so you learn to improve agents the way you’ll actually improve them.
✅Guardrails you workwith.Destructive, staging, commit, push, and PR-create commands are denied outright; sanctioned actions are wrapped in allow-listed skills. You learn to build inside enforceable policy, not around it.
The pipelineplan → build → review → test, with a defect fork when a suite catches a real product bug. Progress is visible on the Jira board through a single parent-story status, one owner per transition
To Do → In Planning → Ready for Dev → In Progress → In Review → In Testing → Done
You’ll practice
Matching tools to intent · planning before doing · delegating execution to keep an orchestrator’s context lean · separating “it compiles” from verified and “review” from security audit · and feeding real findings back into agents so each dogfood failure pre-empts its whole class
of bug next time.
Stack:Python · Pytest · Playwright · pipenv · pytest-html + Allure · custom Claude Code subagents and skills · live Jira Cloud.
Educational course material — designed to be worked through interactively with Claude Code against a sandbox Jira project and a disposable app-under-test.
Who this course is for
⭐ Developers and automation engineers learning to build with Claude Code subagents
