Property search is rarely a simple matter of filtering by price and location. Listings use inconsistent descriptions, important details may be missing, and an appealing property can look different when compared with local market context. This course shows you how to build AI agents that make property research more structured, explainable, and useful while keeping their limitations visible. You will learn to turn a buyer’s or renter’s goals into clear search criteria, connect an agent to listing retrieval tools, and organize property details so results can be compared consistently. You will then design transparent ranking logic that explains trade-offs between factors such as price, location, and property features. For market analysis, you will work with comparable properties and interpret pricing signals without treating incomplete data as certainty. The course progresses from core agent concepts to a practical end-to-end workflow. You will practice defining data requirements, retrieving and evaluating listings, building a property comparison, and checking an agent’s output for relevance, accuracy, and unsupported claims. Each step focuses on a distinct part of the system, so you can understand how the pieces work together rather than treating the agent as a black box. This course is designed for developers, real estate professionals, and product builders who want to apply AI agents to property research. You do not need prior real estate analytics experience, but familiarity with basic AI concepts or software workflows will help. Build a property-search agent that can support better-informed decisions, and start putting it to work on a focused, practical use case.