# Build a unified AI agent architecture with DynamoDB and Bedrock

DevFeed: [Build a unified AI agent architecture with DynamoDB and Bedrock](<https://devfeed.tech/articles/build-a-unified-ai-agent-architecture-with-dynamodb-and-bedrock-4636.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/architecture/build-a-unified-ai-agent-architecture-with-dynamodb-and-bedrock/>)

Author: Dhananjay Karanjkar

Published: 2026-08-21T18:19:23Z

Content type: tutorial

Language: en

Sources: [AWS Architecture Blog](<https://devfeed.tech/sources/aws-architecture-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [amazon-dynamodb](<https://devfeed.tech/tags/amazon-dynamodb.md>), [amazon-opensearch-service](<https://devfeed.tech/tags/amazon-opensearch-service.md>), [ann](<https://devfeed.tech/tags/ann.md>), [api](<https://devfeed.tech/tags/api.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [data-architecture](<https://devfeed.tech/tags/data-architecture.md>), [database](<https://devfeed.tech/tags/database.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [search](<https://devfeed.tech/tags/search.md>), [streams](<https://devfeed.tech/tags/streams.md>), [sync](<https://devfeed.tech/tags/sync.md>), [vector](<https://devfeed.tech/tags/vector.md>)

## AI overview

A tutorial for building an Amazon Bedrock agent that uses one DynamoDB table for structured data and semantic vector search. DynamoDB Streams generates embeddings when content changes, keeping the vector index synchronized.

## Source excerpt

With native vector search in Amazon DynamoDB, you can store vector embeddings alongside your operational data in a single table. This post shows how to build a unified AI agent architecture where an Amazon Bedrock agent uses one DynamoDB table for both structured lookups and semantic search, with a DynamoDB Streams pipeline that keeps embeddings in sync.