---
title: "Agentia Real Estate Agent Project"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Agentia Real Estate Agent Project

Below is a suggested **twelfth project** in your **Agentia** learning path. This scenario focuses on **real estate exploration and recommendation**—building a **Real Estate Advisor Agent** that helps users find and compare properties (homes, apartments, commercial spaces) based on their preferences, budget, and location. This agent can integrate with external property listing services (real or mocked), leverage user preferences, and utilize **LLM-based conversation** to refine search results while preserving a **human-in-the-loop** approval mechanism for major decisions.

---

## Project Overview

1. **Goal**  
   - Develop a **Real Estate Advisor Agent** that assists users by:  
     1. Collecting user requirements (type of property, location, budget, features).  
     2. Searching multiple listing sources to find matching properties.  
     3. Providing curated recommendations and allowing the user to refine or filter further (e.g., “Show me only properties with 3 bedrooms and a garden”).  
   - Maintain a **human-in-the-loop** for final decisions, such as scheduling viewings, requesting more info from property owners, or making an offer.

2. **Key Components**  
   1. **Front-End Orchestration Agent** (existing)  
      - Continues to serve as the main interface for user input and orchestration.  
   2. **Greeting Agent** (existing)  
      - Handles simple greetings.  
   3. **User Preference Agent** (existing)  
      - Stores user-specific criteria like budget ranges, preferred locations, required amenities (e.g., garage, backyard), property type (rental vs. purchase).  
   4. **Knowledge Graph Agent** (optional)  
      - Could store relationships between neighborhoods, local amenities, historical property data, or user’s previous searches.  
   5. **Mail Processing Agent** (optional)  
      - Can handle automated emails to users with newly matched listings, price drop alerts, or appointment confirmations.  
   6. **Real Estate Advisor Agent** **(New)**  
      - Integrates with external property listing APIs (Zillow, Realtor.com, or mock sources).  
      - Uses an **LLM** to interpret user requirements and refine search queries.  
      - Suggests suitable properties, organizes viewings, and ensures major actions require user confirmation.

3. **Value Proposition**  
   - Demonstrates how **Agentia** can unify property data from multiple sources, leverage conversation-based refining of search results, and provide an interactive experience for prospective buyers or renters.  
   - Showcases **LLM-based** query interpretation, balancing structured data (listings) with user-friendly conversation.

---

## 1. Plan the Architecture

1. **Service Layout**  
   - The **Real Estate Advisor Agent** runs as a dedicated service/container.  
   - It connects to:  
     - **Listing APIs** (real or mock), providing property details (location, price, photos, descriptions).  
     - Possibly a **geo-mapping** service (Google Maps or an open-source alternative) for location-based searches.

2. **Communication Patterns**  
   - Continue using **HTTP**, **gRPC**, or message queues for inter-agent communication.  
   - The Advisor Agent’s **LLM function-calling** might define tasks like:  
     - **SearchProperties**(location, budget, propertyType, amenities)  
     - **RefineSearch**(existingResults, newCriteria)  
     - **ScheduleViewing**(propertyId, userAvailability)  
     - **RequestOwnerInfo**(propertyId)

3. **Human-in-the-Loop**  
   - When the user wants to make a formal inquiry or request a viewing, the system returns a **draft** for confirmation.  
   - The user must approve before personal info is shared or any appointment is booked.

---

## 2. Real Estate Advisor Agent

### 2.1 Responsibilities

1. **Collect and Interpret User Requirements**  
   - Ask clarifying questions about location, price range, property size, special features (pool, garage, etc.).  
   - Possibly incorporate user’s future plans (e.g., “I need a home office,” “I want to be near schools”) from the **User Preference Agent**.

2. **Property Search and Aggregation**  
   - Perform queries to property listing APIs with relevant filters.  
   - Collate the results (e.g., address, price, photos, key features) into a structured format.  
   - Use the LLM to create a summarized, user-friendly response (e.g., “This 3-bedroom house is in your budget, located in a quiet neighborhood, with a small backyard.”).

3. **Refining Recommendations**  
   - Let the user refine or filter (“Only show me properties with at least 2 bathrooms,” “I prefer older houses with character,” etc.).  
   - The LLM translates these constraints into new search queries or sub-filters.

4. **Scheduling and Communication**  
   - If the user is interested in a property, prompt them to schedule a viewing or request more info from the seller/agent.  
   - Use the **Mail Processing Agent** or direct scheduling integration to propose times, confirm appointments, or send the user’s contact info to the listing’s owner.

5. **Draft Approval**  
   - For final actions (scheduling a meeting, requesting a price negotiation), the agent returns a **draft**.  
   - The user must approve before any personal info is shared or a request is formally sent.


## 3. Front-End Orchestration Agent: Extended Logic

1. **Identify Real Estate Queries**  
   - If the user’s requests involve property searching, rent/buy decisions, or scheduling viewings, delegate to the **Real Estate Advisor Agent**.

2. **Draft Confirmation**  
   - When the user wants to finalize an action (book a viewing, request an owner’s contact), the **Front-End** presents a summary of the details.  
   - User must approve before the agent proceeds.

3. **Fallback**  
   - If the user’s request is unclear (“I want something cheap in a good area”), the agent might ask about a specific budget range or location preference.

---

## 4. Demonstration Scenario

1. **User**: “Hello!”  
   - **Front-End** → **Greeting Agent** → “Hi there! How can I help you?”  

2. **User**: “I’m moving to NYC for work. Need a 1-bedroom apartment near Midtown. Budget up to \$3,000/month.”  
   - **Front-End** → **Real Estate Advisor Agent**  
   - The agent calls `SearchProperties(location='Midtown, NYC', propertyType='apartment', bedrooms=1, maxRent=3000)` from the listing API.  
   - Returns a summary of 3-5 matches.  

3. **User**: “Do any of these have a gym or fitness center in the building?”  
   - **Agent** refines results using the amenity filter.  
   - Possibly 2 matches remain, displayed to the user with more details.  

4. **User**: “I’d like to schedule a viewing for the second listing.”  
   - **Front-End** → The agent calls `ScheduleViewing(propertyId=XYZ, userAvailability=someSlots)`.  
   - Returns a **draft** indicating the date/time request.  
   - User approves, and the agent finalizes the request with the landlord or listing agent.  

5. **User**: “Great, let me know if there’s any update.”  
   - The conversation can end, or the system can use the **Mail Processing Agent** to notify the user of the landlord’s response.

---

## 5. Deployment and Testing

1. **Local or Cloud Setup**   
   - Integrate with either a **real listing API** (Zillow, Realtor.com) if you have developer access, or use a **mock** dataset for testing.

2. **LLM Function-Calling**  
   - Tools might include:
     - **SearchProperties**(location, budget, propertyType, featureFilters)  
     - **RefineSearch**(existingResults, newCriteria)  
     - **ScheduleViewing**(propertyId, userAvailability)  
     - **RequestOwnerInfo**(propertyId)
   - Ensure the LLM can parse the user’s descriptive requests and convert them into structured search/filter parameters.

3. **Observability**  
   - **Real Estate Advisor Agent**: Log each search request, refinement, selected properties, and scheduling actions.  
   - **Front-End**: Log user approvals or rejections for scheduling.

4. **Error Handling**  
   - If no properties match, prompt the user to adjust budget or location.  
   - If the scheduling API fails, handle gracefully (e.g., “The owner’s scheduling portal is temporarily unavailable—try again later”).

---

## 6. Possible Enhancements

1. **Location-Based Analytics**  
   - Offer neighborhood stats like crime rates, school quality, walkability scores, or commute times to the user’s workplace.  
   - Possibly integrate with a **Knowledge Graph Agent** storing relevant local data.

2. **Personalized Alerts**  
   - Use the **Mail Processing Agent** to send alerts when a new listing matches the user’s saved search criteria, or when an existing listing’s price drops.

3. **Market Insights**  
   - Provide average price trends, days-on-market data, or recently sold comps in the user’s target area.  
   - Offer advice on negotiating rent or purchase price.

4. **Virtual Tours**  
   - If available, link to virtual tour videos or 3D models for each property.  
   - The agent can ask if the user wants a virtual tour link or an in-person viewing.

5. **Offer and Negotiation**  
   - If focusing on buying, the system could help the user draft an offer or letter of intent, returning it as a **draft** for approval.  
   - Integrate a mock or real estate contract template for advanced demos.

6. **Multiple Users or Co-Buyers**  
   - Allow multiple users (roommates, partners) to collaborate on the same search.  
   - Possibly maintain shared preferences or a group chat for deciding on a property.

---

## Conclusion

This **twelfth project**—the **Real Estate Advisor Agent**—illustrates how **Agentia** can:

- **Aggregate** and interpret property listings from multiple sources.  
- **Conversationally** refine search results using an LLM, matching nuanced user requirements.  
- **Orchestrate** final actions (scheduling viewings, sending contact requests) with a **human-in-the-loop** flow.  
- **Integrate** with existing agents (Preferences, Knowledge Graph, Mail Processing) for richer user experiences and data management.

By building this **Real Estate Advisor Agent**, you show how **Agentia** can seamlessly handle high-stakes searches (like finding a home) in a transparent, user-controlled, and context-aware manner.
