UDEMY.AI.Test.Architect.LangChain.LangGraph.RAG.MultiAgent.QA.2026.BOOKWARE-MiMiR

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eBooks/Audiobooks

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MiMiR

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8,55 GB

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48

Date

2026-10-13

NFO

Mimir, Keeper of the Well of Wisdom
  AI Test Architect: LangChain, LangGraph, RAG & MultiAgent QA
  https://www.udemy.com/course/ai-test-automation-langchain-
  langgraph-rag-ai-agents/
  Year         : 2026
  Language     : English
  Level        : All Levels
  Category     : IT & Software
  Subcategory  : Other IT & Software
  Duration     : 18h 29m
  Lectures     : 107
  Rating       : 4.6/5 (17 reviews)
  Students     : 383
  INSTRUCTOR(S)
  HEADLINE
    Master LangChain, LangGraph, RAG & Multi-Agent Systems. Build
    AI-powered QA workflows and an AI QA Copilot for testing
  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
  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
  WHO IS THIS COURSE 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
  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.
  COURSE CONTENT
  Chapter 1: Introduction
    1. Introduction
    2. Pre-requisite
    3. LangChain vs LangGraph vs LangSmith
    4. LangChain vs LangGraph vs LangSmith - Image
    5. Download the Code used in all lectures
  Chapter 2: LangChain Fundamentals
    6. Creating very first Langchain Agent
    7. Handwritten Notes
    8. Invoke Methods
    9. Stream Methods
    10. Handwritten Notes
  Chapter 3: System prompts and Tools
    11. System Prompts
    12. Handwritten Notes
    13. Parallel Agent Calls
    14. Handwritten Notes
    15. Giving Tools to the Agent
    16. Handwritten Notes
    17. Getting the paid API key
    18. Assigning Multiple tools to the AI Agent
    19. Handwritten Notes
  Chapter 4: Defining Response schema
    20. Structured output with ZOD schema
    21. Adding multiple schemas
    22. Handwritten Notes
  Chapter 5: AI Agent with Memory and Model Configuration
    23. Agent without Memory
    24. Adding memory to the Agent
    25. Handwritten Notes
    26. Adding Advance memory to recall conversation
    27. Handwritten Notes
    28. Clear Memory
    29. Configuring Model Parameters
  Chapter 6: Human in Loop workflow
    30. Human-in-the-Loop ? Add Human Approval to Your Agents
    31. Handwritten Notes
  Chapter 7: AI Agent Controls PlayWright ? Real Browser Testing
    with LangChain
    32. Real Browser Testing with LangChain
  Chapter 8: LangGraph Fundamentals
    33. Presentation used for LangGraph
    34. Introduction to LangGraph
    35. Building Controlled Agent Workflows
    36. Conditional Edges
    37. Cycles / Loops
    38. Human in the Loop
  Chapter 9: Langgraph Studio & LangSmith
    39. Configuring the LangGraph studio
    40. Configuring Langsmith tracing
  Chapter 10: ReAct + MessageAnnotation
    41. Building a ReAct AI Agent
  Chapter 11: Core RAG (Retrieval-Augmented Generation)
    Fundamentals
    42. What is RAG and why it is useful?
    43. Download the presentation
    44. Core Components of RAG
    45. Download the presentation
    46. Creating the first RAG Pipeline
    47. Installing Ollama embedding model and llm
    48. Adding comments for future reference
    49. RAG with Text Files
    50. RAG Pipeline querying 40 Pages SRS Document
    51. SRS Document download
  Chapter 12: Agentic RAG
    52. Present used in the upcoming lectures
    53. What is Agentic RAG? (Conceptual Understanding)
    54. When should the Agent reterieve information
    55. Agent RAG Implementation
    56. Agent RAG with multiple tools
  Chapter 13: MCP (Model Context Protocol) for AI Agents
    57. What is MCP?
    58. What is MCP - PDF file
    59. MCP Architecture explained
    60. MCP Architecture explained - PDF file
    61. Creating a MCP server from scratch
    62. MCP call with LangGraph agent
    63. Building Playwright MCP Server
    64. Connect Multiple MCP servers to the LangGraph agent
  Chapter 14: Multi-Agent systems, Orchestration & Guardrails
    65. What are Multi-Agent systems?
    66. What are Multi-Agent systems - PDF file
    67. Understanding the Supervisor + Worker pattern
    68. Understanding the Supervisor + Worker pattern - PDF File
    69. Building a Simple Multi-Agent System
    70. Building a Multi Agent Test Automation workflow
    71. Agent Communication and HandOff
    72. Routing between Agents - Orchestration
    73. Complete Multi-Agent Testing Workflow
    74. Guardrails
  Chapter 15: Capstone Project - AIQACopilot
    75. Download the Capstone Project
    76. AI QA Copilot capstone project overview
    77. AI QA Copilot capstone project overview - PDF
    78. Creating the Base Agent
    79. Add RAG for Requirements Q&A
    80. Adding Test Case Generator
    81. Adding MCP Tools
    82. Adding Memory and Guardrails
    83. Designing the Final QA AI Copilot
    84. Demostrating the project using LangSmith
  Chapter 16: Typescript Fundamentals (Optional - Taken from the
    Playwright Typescript course)
    85. Introduction to Typescript
    86. Pre-requisite
    87. Naming Conventions, Variables and Datatypes
    88. More Types
    89. Operators
    90. Concatenation Operators
    91. Functions
    92. Arrow Functions and Function Overloading
    93. Control Flow statements - IfElse, SwitchCase
    94. Loops
    95. Arrays
    96. 2D Arrays
    97. Tuples
    98. Objects
    99. OOPS - Classes
    100. OOPS - Access modifiers and Variable scopes
    101. OOPS - Export and Import
    102. OOPS - Inheritance
    103. OOPS - Abstract Classes
    104. OOPS - Interfaces
    105. Getter and Setter
    106. Exception Handling
  Chapter 17: BONUS
    107. BONUS
  DATES
  Published    : 2026-08-23
  Last Updated : 2026-09-02
If you fear the truth, dont come to my well.

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