LangChain – Develop Controlled AI Agent with LangChain & RAG | Udemy

Learn how to design, build, and deploy controlled Business AI Agents using LangChain, RAG (Retrieval-Augmented Generation), OpenAI LLMs, and a production-ready backend with FastAPI.

This course focuses on how real AI agent systems are structured in modern products and startups. You will learn how to combine agents, chains, prompts, schemas, and vector databases to create AI systems that can reason, plan, retrieve knowledge, and validate outputs in a controlled and reliable way.

*** What You Will Learn ***

• The difference between LLMs and AI Agents
• Why LangChain is used for agent orchestration
• How to design controlled AI agents for business use cases
• Prompt engineering for business, planning, marketing, emails, and tasks
• Using schemas to enforce structured AI responses
• Building chains and agent executors
• Understanding RAG (Retrieval-Augmented Generation) in depth
• Uploading files and converting them into usable AI context
• Creating embeddings and storing them in a vector database
• Performing similarity search using retrievers
• Managing context and solving RAG memory issues
• Reviewing and validating AI responses before final output
• Viewing and managing vectors in ChromaDB
• Adding security middleware to your AI backend
• Running the complete AI agent using FastAPI

*** Project You Will Build ***

In this course, you will build a complete Business AI Agent system that includes:

• A Business Agent for understanding requirements
• A Planning Agent for structured decision-making
• A Marketing Agent for strategy and content generation
• An Email Agent for professional communication
• A Tasks Agent for structured task generation
• A RAG (Retrieval-Augmented Generation) pipeline using a vector database
• Response review and validation before final output
• A backend API built with FastAPI

By the end of the course, you will understand how multiple agents work together in a real-world AI system.