Cloud-Plattformen · 21.08.2026, 18:31 UTC
Build a unified AI agent architecture with DynamoDB and Bedrock
| Schweregrad | info |
|---|---|
| Kategorie | Cloud-Plattformen |
| Quelle | AWS Architecture ↗ |
| Veröffentlicht | 21.08.2026 UTC |
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Teams building AI agents on AWS often face a fragmented data architecture: operational data lives in Amazon DynamoDB while vector embeddings for semantic search sit in a separate, purpose-built vector store. This duplication increases infrastructure cost, adds synchronization complexity, and widens the window for stale retrieval results. With the general availability of native vector search in Amazon DynamoDB (launched August 5, 2026), you can now store embeddings alongside your operational data in the same table. You query them using the SearchVectors API operation. In this post, I show you how to build a unified AI agent architecture where an Amazon Bedrock agent uses a single DynamoDB table for both structured lookups and semantic similarity search. The agent calls AWS Lambda action groups that invoke SearchVectors for natural language retrieval and standard DynamoDB APIs for create, read, update, and delete (CRUD) operations. An Amazon DynamoDB Streams pipeline automatically generates embeddings using Amazon Titan Text Embeddings V2 whenever content changes. This keeps the vector index synchronized without manual intervention. Use case Consider a technical knowledge management platform where a team maintains hundreds of internal documents: runbooks, architecture decision records, and troubleshooting guides. Team members interact with a conversational agent to find relevant content (“What’s our retry strategy for payment failures?”), retrieve specific documents by ID, or update existing entries. Without native vector search, this architecture requires a DynamoDB …