What you'll learn:
- Build real AI agents using LangChain and Google Gemini that can reason, use tools, and complete tasks autonomously
- Design agent architectures using ReAct patterns, tool calling, and structured decision making
- Implement short-term and long-term memory in AI agents using databases and embeddings for personalized experiences
- Create and manage agent tools for web search, weather, finance, document analysis, and external APIs
- Apply prompt engineering techniques to control agent behavior, improve output quality, and guide tool usage
- Add safety layers such as guardrails, human-in-the-loop approval, and middleware controls to prevent errors and misuse
- Stream real-time responses and generate structured outputs from AI agents in production-style applications
- Secure AI agents with sandboxed code execution to prevent file deletion, credential leaks, and system risks
- Build REST APIs for AI agents using FastAPI with validation, CORS, and production-ready patterns
- Develop full-stack AI applications using Streamlit connected to LangChain agents
- Deploy AI agents on AWS EC2 and configure them for real-world access and scalability
Welcome to the most comprehensive Agentic AI projects course on Udemy for 2026 — focused on building, deploying, and scaling real-world AI agents in production.
Learn to architect and ship 10 production-grade AI Agent projects using LangChain v1, LangGraph, Google Gemini 3, MCP (Model Context Protocol), FastAPI, Streamlit, and AWS EC2 — the exact stack powering the next generation of autonomous AI systems.
This course is not another "build a chatbot" tutorial. It is the production deployment companion you need after learning LangChain fundamentals. Every module ends with working, deployable code you can adapt for your own product, client, or portfolio.
Who is teaching this course
I'm Laxmi Kant Tiwari, founder of KGP Talkie, with over a decade of AI and Machine Learning industry experience including senior AI engineering at Linedata. I've taught 160,000+ students across YouTube and Udemy, and I build and deploy the exact systems I teach. No fluff, no filler, no recycled Medium articles — just production-grade code and architecture you can ship.
The 10 Production AI Agent Projects You Will Build
Live Hotel Search Agent — Airbnb MCP server integration with real-time search
Travel Planner Agent — Google Calendar MCP with memory and multi-tool orchestration
Code Execution Agent — Secure E2B sandbox for data analysis (Apple, Google, IMDB, Titanic datasets)
Google Sheets + Finance Analyzer Agent — Yahoo Finance MCP with Sheets read and write
Gmail Daily Briefing Agent — Automated email summarization and action extraction
Personal AI Assistant on AWS EC2 — LangChain Agent Chat UI deployed to production
AI Agent REST API Gateway — FastAPI with Pydantic, CORS, and Swagger UI
Full-Stack AI Assistant — Streamlit + FastAPI with real-time response streaming
Cloud-Deployed Full-Stack Agent — End-to-end AWS EC2 deployment with security hardening
MySQL E-commerce Analyst Agent — TiDB + MCP + FastAPI streaming server
What Makes This Course Different
LangChain v1 and Gemini 3 — the freshest possible stack; most courses still teach deprecated v0.x APIs
Real MCP server integrations — not toy examples. Airbnb, Gmail, Google Calendar, Google Sheets, Yahoo Finance, MySQL
Actual AWS EC2 deployment walkthroughs — from instance launch to production security configuration
Production patterns — model fallback, error handling, guardrails, HITL, streaming, context offloading
26+ hours of hands-on video — every line of code explained, every architecture decision justified
