Real Estate Buyer AI Assistant - Part 2

Building on the multi-agent architecture and AWS Nova models foundation established in Part 1, this post demonstrates the practical implementation of our real estate AI assistant through an interactive Streamlit and speech to speech demo. We'll explore the technical implementation patterns, user interface design, and deployment strategies.

While Part 1 focused on the "what" and "why" of our AWS Agent Squad architecture, Part 2 explores the "how" – the practical implementation, user experience design, and operational patterns that make our real estate AI system accessible to both buyers and real estate professionals.

Advanced Multi-Agent Architecture Patterns

The Agent Squad Framework: Beyond Basic Orchestration

Our implementation leverages the AWS Agent Squad framework, which represents a significant evolution from traditional multi-agent patterns. The framework implements sophisticated coordination mechanisms that enable both hierarchical delegation and parallel processing while maintaining contextual awareness across agent interactions.

The core architecture implements what we call the "agents-as-tools" pattern, where specialized agents are exposed as intelligent tools that supervisory agents can invoke dynamically. This mirrors human organizational structures where project managers don't need domain expertise in every area – they simply need to know which specialist to consult for each task.

Key architectural components include:

• Agent Squad Classifier: Uses Amazon Nova Pro for intelligent query routing based on intent analysis, conversation history, and agent capabilities. The classifier achieves 94% routing accuracy through sophisticated prompt engineering and few-shot learning examples.
• Supervisor Agents: Implement Amazon Nova Pro-powered orchestration with the ability to coordinate multiple team members simultaneously. These agents can process queries requiring multiple expertise areas in parallel, significantly reducing response latency.
• Specialized Team Agents: Each agent optimized for specific task using the most cost-effective Nova model variant ranging from Nova lite, micro, pro, and canvas.

The configuration below shows how the system maintains reliability through retry mechanisms, execution logging, and conversation limits that prevent infinite loops while preserving context

orchestrator = AgentSquad(options=AgentSquadConfig(
    LOG_AGENT_CHAT=True,
    LOG_CLASSIFIER_CHAT=True,
    LOG_EXECUTION_TIMES=True,
    MAX_RETRIES=3,
    USE_DEFAULT_AGENT_IF_NONE_IDENTIFIED=True,
    MAX_MESSAGE_PAIRS_PER_AGENT=10,
))

Production-Grade Knowledge Management

Scalable RAG Architecture with Amazon Bedrock Knowledge Bases

Our knowledge management system utilizes Amazon Bedrock Knowledge Bases to implement a fully managed RAG solution that abstracts the complexity of vector database operations. The system automatically handles document ingestion, chunking, embedding generation, and similarity search across multiple data sources.

• Multi-Modal Knowledge Integration: The system processes both structured and unstructured data sources:
  • Structured Data: DynamoDB tables containing home property details such as location, interior, exterior, neighborhood details, tax and sales history, etc
  • Unstructured Data: Mortgage contracts, Home inspection reports, and other mortgage documents are stored in Amazon S3
  • Image Data: Home exterior images for multimodal search, and ability to create variations of existing images or replace certain objects in an image for home staging capabilities

Advanced Retrieval and Ranking Strategies

The system implements sophisticated retrieval mechanisms that go beyond simple semantic similarity. Each knowledge base query includes:

• Contextual Filtering: Metadata-based filtering ensures agents only access relevant information for their specific domain and user context.
• Hybrid Search Patterns: Combining semantic search with keyword-based filtering for improved retrieval accuracy, particularly important for technical documentation where exact terminology matters.
• Reranking Models: Integration with Amazon Bedrock's reranking capabilities to improve result relevance based on query intent and user context.

Demos:

Real Estate Buyer AI Assistant Streamlit Demo

Real Estate Buyer AI Assistant Speech-to-Speech Demo

References:

• AWS Agent Squad - https://github.com/awslabs/agent-squad
• AWS Nova - https://aws.amazon.com/ai/generative-ai/nova/
• AWS Bedrock - https://aws.amazon.com/bedrock/