In this project, I build an AI Travel Agent that behaves like a real “agentic” system: it takes your preferences (budget, theme, visa constraints, hotel ratings, etc.), searches across options, and then optimizes for cost and fit. The goal isn’t just to generate a pretty itinerary—it’s to design an agent workflow that can reason step-by-step: find flights, shortlist hotels, pick attractions, and finally stitch everything into a day-by-day plan. I’m using Gemini Flash for fast iterations, and I focus heavily on the system design thinking so you can reuse the same patterns for other agents. I also show a full demo of the travel planner in action and break down the building blocks you need before you start—especially understanding AI agents and common agentic design patterns. If you’ve been confused about how “autonomous AI” actually becomes a working product, this video is the blueprint: inputs → tool-driven search → filtering + ranking → itinerary synthesis. And if you want to go deeper, I’ve linked the prerequisites and the full source code so you can clone, run, and extend the agent into your own production-grade GenAI project.