Build a unified AI agent architecture with DynamoDB and Bedrock

This article demonstrates how to build a unified AI agent architecture using DynamoDB's native vector search capability combined with Amazon Bedrock, eliminating the need for separate vector databases.

• Store embeddings alongside operational data in a single DynamoDB table with native vector search
• Use Bedrock agents with Lambda action groups to invoke SearchVectors API for semantic search and standard CRUD operations
• Automate embedding generation via DynamoDB Streams pipeline using Amazon Titan Text Embeddings V2
• Design single-table schema with vector index partitioned by category or tenant for efficient queries
• Implement semantic search with COSINE distance scoring and operational lookups in unified architecture
• Apply security best practices including least-privilege IAM, encryption at rest/transit, and tenant isolation via SearchSchema

This pattern reduces infrastructure complexity for applications already using DynamoDB by enabling real-time semantic search without managing separate vector stores or synchronization pipelines.