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Keep shoppingAI Test Architect: LangChain, LangGraph, RAG & MultiAgent QA
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AI Test Architect: LangChain, LangGraph, RAG & MultiAgent QA
Master LangChain, LangGraph, RAG & Multi-Agent Systems. Build AI-powered QA workflows and an AI QA Copilot for testing
Created byRahul Arora
Last updated 9/2026
English
English [Auto],
What you'll learn
- Build production-grade AI applications in TypeScript using LangChain and Anthropic's Claude models
- Master LCEL (LangChain Expression Language) to compose chains, run steps in parallel, and stream responses
- Generate reliable, type-safe structured output using Zod schemas and Claude's tool-calling API
- Create custom tools that let an LLM read files, call APIs, run shell commands, and drive a browser
- Build complete RAG pipelines with document loaders, chunking strategies, embeddings, and ChromaDB
- Improve retrieval quality with metadata filtering, multi-query retrieval, re-ranking, and compression
- Debug, trace, and cost-monitor every LLM call using LangSmith
- Run automated evaluations on prompts — regression testing for AI, using skills you already have as a QA engineer
- Prototype AI workflows visually with Langflow and export them as working APIs
- Integrate Playwright with LangChain to generate specs and self-heal broken selectors
- Understand why simple agents fail — and use LangGraph to build stateful, cyclic, multi-actor systems instead
- Implement human-in-the-loop approval gates, checkpointing, and durable agent state
- Build a full Autonomous QA Agent: explores an app, plans tests, writes Playwright specs, runs them, diagnoses failures, and heals itself in a loop
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Course content
17 sections • 107 lectures • 18h 30m total length
Requirements
- Experience with any test automation framework (Playwright, Selenium, Cypress) is helpful but not required
- Typescript basics is covered from the scratch in the course
- Free accounts for LangSmith and ChromaDB (local install, no cost)
- No prior AI, machine learning, or Python experience needed
Description
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 over your own test suite, so you can ask "do we already have a test for this?"
Multi-Agent systems, Orchestration & Guardrails and many more practical concepts
Honest expectations
You'll see agents fail. We walk through what breaks, why it breaks, and how to build guardrails around it. Non-determinism is uncomfortable for testers — we address that directly instead of pretending it away.
By the end, you'll have shipped multiple working projects and one capstone that very few engineers can demo.
Enroll now and start building.
Who this course is for:
- Anyone who wants to learn LangChain and build autonomous AI Agents
- SDETs who want to build AI-powered testing tools
- QA automation engineers transitioning into AI/ML engineering
- TypeScript/JavaScript developers learning LangChain without switching to Python
- Test leads evaluating AI agents for their team
- Anyone who knows TypeScript and wants to build real autonomous agents, not chatbot demos