AI Test Architect: LangChain, LangGraph, RAG & MultiAgent QA

About this course

Every QA automation engineer is asking the same question right now: how do I move into AI before AI moves past me?

This course is the answer — and it's built specifically for you, not for Python data scientists.

You already understand test frameworks, assertions, CI pipelines, and debugging flaky systems. Those are exactly the skills AI engineering needs. What's missing is the LangChain layer — and that's what you'll build here, entirely in TypeScript, using Anthropic's Claude models.

We start from absolute zero on LangChain. Your first API call. Your first chain. Your first tool. Then we go all the way to a multi-actor autonomous agent with cycles, persistent state, and human approval gates.

What makes this course different

Most LangChain courses stop at a chatbot.

This one is built for real testing workflows.

By the end, you will design and build an AI QA Copilot — a practical LangGraph-powered system that can:

Answer questions from requirements and SRS documents using RAG Generate structured test cases from user stories Create test data for automation scenarios Use tools and MCP servers for reusable QA utilities Coordinate multi-agent workflows like Generator, Reviewer, and Supervisor Apply memory, routing, and guardrails for more reliable agent behavior

This is not just theory. This is a portfolio-ready AI testing project you can showcase in interviews and real QA work.

You'll also master the tools teams actually pay for

LangSmith isn't an afterthought here — you'll use it from Section 8 onward to trace every agent decision, catch failures, and run prompt evaluation suites. As a QA engineer, you'll recognize it immediately: it's regression testing for LLMs.

VectorDB powers a real RAG project — a searchable knowledge base o

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