本课程是一个基于 Anthropic Claude Agent SDK 与 TypeScript 的全栈工程实战指南，旨在通过“不改模型、只调架构”的硬核调优，将一个真实客户支持智能体的任务成功率从 56% 提高到 88%，完美对齐 CCDV-F 认证考试 的实战标准。
课程摒弃了 PPT 概念灌输，完全以真实运行账单和量化指标为驱动，手把手带你从零构建一套不依赖人工、通过断言自主捕获 Bug 的自动化评测桩（Eval Harness）。在核心演进中，你将通过最小特权工具（Least-Privilege Tools）与动态上下文包（Context Pack）将胜率直接拉升至 84%；随后引入硬性 Token 预算下的跨会话持久内存，并用一致性裁判（Coherence Judge）根治智能体失忆；在成本与运维端，课程通过提示词缓存、智能模型路由与预算熔断器揭秘真正能省钱的方案，并最终为智能体套上能抵御 8 种真实注入攻击的安全防护层，以 Headless（无头微服务） 形式接入全链路可观测性追踪（Observability Traces）成功上线。
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 920.00 MB | Duration: 2h 14m
Build a real Claude Agent SDK agent from 56% to 88% success and ship it. Maps to the CCDV-F exam.
What you’ll learn
Build a working Claude Agent SDK support agent from scratch and measure its real task success rate: 56 percent at baseline, 88 percent by the end
Build an eval harness that grades every run against a fixed golden set instead of hand-grading, and catches real bugs that a clean exit code hides
Design least-privilege tools and a context pack that lifted task success from 56 to 84 percent without changing the underlying model
Add memory that survives across sessions inside a hard token budget, and a coherence judge that catches an agent that looks fine but has amnesia
Apply three cost controls (caching, model routing, a budget ceiling) and measure which ones actually pay for themselves versus which make the bill worse
Ship the agent headless with a security layer that survives 8 real attacks, observability traces, and a real incident response, then reuse the architecture
Requirements
Comfortable reading and writing basic TypeScript
A terminal and Node.js installed
An Anthropic API key (a free-tier key is enough to follow along)
No prior experience building AI agents required
Description
This course builds one real production AI agent, start to finish, using the Claude Agent SDK. You watch a single support agent get built, measured, and shipped, lecture by lecture, with every number pulled from actual run receipts instead of slides.It starts honest. The first version is a one-evening agent that answers real support tickets and gets 56 percent of them right. From there, each lecture changes exactly one thing: tools, an eval harness, context, memory, a subagent, cost controls, security, deployment, and observability. Then it measures what that change was actually worth.Most lectures report a win. At least one reports that the fix everyone recommends made the bill worse. Both get shown on screen, with the receipts, because a course that only shows wins is not teaching you how production actually works.The course also maps to the Claude Certified Developer (CCDV-F) certification blueprint. The agentic loop, tool design, evals, context engineering, memory, subagents, cost, security, and deployment are covered in the order the exam expects, built as working code instead of slides.What makes this course different:- Every metric on screen comes from a graded run against a fixed set of support tickets, never typed in by hand- One companion GitHub repo with a checkpoint branch per lecture, so you can run every version of the agent yourself and check the numbers against your own receipts- At least one lecture shows a popular best practice failing, with the real numbers, instead of only showing what worked- A full working eval harness, cost guard, and security layer you can reuse to cold-start a second agent from day one- Built for developers who already know basic TypeScript. No prior agent-building experience assumedBy the finale the agent holds steady at 88 percent task success, with a working eval harness, a cost guard, a security layer that has been attacked on camera and held, and a full architecture you can reuse for your next agent.You need TypeScript and a terminal. Everything is built from the public companion repo, branch by branch, so you can run every version of the agent yourself instead of taking my word for any of it.
Developers who have played with the Claude Agent SDK or Claude Code and want to know what separates a demo from something you can run in production,Anyone preparing for the Claude Certified Developer (CCDV-F) certification who wants the concepts as working code, not just exam theory,Engineers who own, or will own, an AI agent in production and need a working playbook for evals, cost, security, and incident response
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