本课程专注于企业级 AI Agent 实战，以 LangChain 和 LangGraph 为框架，结合 Azure AI Foundry、Terraform 及 MCP 标准，教授如何构建具备自主决策、复杂推理（ReAct架构）和外部数据调用的生产级智能体。课程深入讲解了沙箱代码执行、Cosmos DB 记忆持久化、Human-in-the-loop 安全机制及 LangSmith 全链路追踪，全方位打造高安全、可观测的智能体应用。
Published 6/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 24m | Size: 1.26 GB
Build production-ready AI agents with LangChain, Azure AI Foundry, MCP servers, tools, memory, and orchestration
What you’ll learn
Build AI agents using LangChain and LangGraph with Azure AI Foundry
Deploy and configure LLMs on Azure using Terraform and secure API access
Design agents with tools, function calling, and the ReAct reasoning loop
Connect agents to data using MCP (Microsoft Learn + web search)
Execute Python and shell code safely using sandboxed environments
Add memory to agents using Azure Cosmos DB for multi-turn conversations
Implement human-in-the-loop workflows and model guardrails
Observe, debug, and optimize agents using LangSmith tracing
Requirements
Basic knowledge of Python (functions, APIs, packages)
Familiarity with REST APIs and JSON is helpful
Azure account (free tier works) to deploy models and services
Basic understanding of AI/LLMs concepts is a plus (not required)
Development environment (VS Code or Jupyter Notebook)
Curiosity to build real-world AI agents
Description
In this hands-on course, you will learn how to build modern AI agents from scratch usingLangChain andMicrosoft Azure. Instead of focusing only on theory, this course is designed as a practical journey where every concept is demonstrated through real implementations you can run and adapt in your own projects.
We start by understanding why AI agents are the next evolution beyond simpleLLMapplications, and how they combine reasoning with action using tools, memory, and external systems. From there, you will deploy your first model inAzure AI Foundry usingTerraformand build a workingReAct agent capable of making decisions and executing tasks.
As the course progresses, you will extend your agents with powerful capabilities such astool and function calling, integration withModel Context Protocol (MCP) servers, and real-time access to external data like Microsoft documentation and web search. You will also explore how to give your agents execution capabilities usingsandboxed environments with Python and shell tools.
You will learn how to implement memory usingAzure Cosmos DB, manage multi-turn conversations, and introducehuman-in-the-loop workflows to safely control agent behavior. Advanced topics include middleware hooks, observability withLangSmith, and designingmulti-agent architectures where specialized agents collaborate to solve complex problems.
By the end of this course, you will be able to design, build, and operate production-grade AI agents that integrate seamlessly with enterprise-grade Azure services.
What you’ll build
– ReAct-based AI agents on Azure
– Agents with tools, memory, and MCP integrations
– Multi-agent orchestration systems
– Production-ready observable AI workflows
Who this course is for
Python developers who want to build real AI agents
Azure engineers and cloud architects exploring AI workloads
AI/ML practitioners looking to move from prompts to autonomous agents
Developers interested in LangChain, MCP, and agent orchestration
Anyone curious about building production-ready AI systems on Azure
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