Agentic AI Projects: FastAPI, MCP, AWS Deploy & Gemini 3

<div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>Who is teaching this course</div><div><br></div><div>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.</div><div><br></div><div>The 10 Production AI Agent Projects You Will Build</div><div><ul><li><span style="font-size: 1rem;">Live Hotel Search Agent — Airbnb MCP server integration with real-time search</span></li><li><span style="font-size: 1rem;">Travel Planner Agent — Google Calendar MCP with memory and multi-tool orchestration</span></li><li><span style="font-size: 1rem;">Code Execution Agent — Secure E2B sandbox for data analysis (Apple, Google, IMDB, Titanic datasets)</span></li><li><span style="font-size: 1rem;">Google Sheets + Finance Analyzer Agent — Yahoo Finance MCP with Sheets read and write</span></li><li><span style="font-size: 1rem;">Gmail Daily Briefing Agent — Automated email summarization and action extraction</span></li><li><span style="font-size: 1rem;">Personal AI Assistant on AWS EC2 — LangChain Agent Chat UI deployed to production</span></li><li><span style="font-size: 1rem;">AI Agent REST API Gateway — FastAPI with Pydantic, CORS, and Swagger UI</span></li><li><span style="font-size: 1rem;">Full-Stack AI Assistant — Streamlit + FastAPI with real-time response streaming</span></li><li><span style="font-size: 1rem;">Cloud-Deployed Full-Stack Agent — End-to-end AWS EC2 deployment with security hardening</span></li><li><span style="font-size: 1rem;">MySQL E-commerce Analyst Agent — TiDB + MCP + FastAPI streaming server</span></li></ul></div><div><span style="font-size: 1rem;">What Makes This Course Different</span></div><div><ul><li><span style="font-size: 1rem;">LangChain v1 and Gemini 3 — the freshest possible stack; most courses still teach deprecated v0.x APIs</span></li><li><span style="font-size: 1rem;">Real MCP server integrations — not toy examples. Airbnb, Gmail, Google Calendar, Google Sheets, Yahoo Finance, MySQL</span></li><li><span style="font-size: 1rem;">Actual AWS EC2 deployment walkthroughs — from instance launch to production security configuration</span></li><li><span style="font-size: 1rem;">Production patterns — model fallback, error handling, guardrails, HITL, streaming, context offloading</span></li><li><span style="font-size: 1rem;">26+ hours of hands-on video — every line of code explained, every architecture decision justified</span></li></ul></div>

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