---
title: "Build a unified AI agent architecture with DynamoDB and Bedrock"
description: "AI agent architectures on AWS frequently divide operational records and vector embeddings across separate databases, creating synchronization lag and infrastructure overhead. To resolve this fragmentation, a single DynamoDB table can store operational document attributes alongside embeddings by leveraging native vector search. An Amazon Bedrock agent directs incoming queries to AWS Lambda action groups, which run standard CRUD operations or semantic searches against the DynamoDB SearchVectors API. When items change, a DynamoDB Streams pipeline uses Amazon Titan Text Embeddings V2 to generate new embeddings and write them back to the table. This unified approach eliminates standalone vector databases while keeping semantic indexes synchronized with operational updates."
---

# Build a unified AI agent architecture with DynamoDB and Bedrock

[AWS](https://yomu.fyi/company/aws) · Dhananjay Karanjkar · Aug 21, 2026

**Type:** Problem & solution

## Summary

AI agent architectures on AWS frequently divide operational records and vector embeddings across separate databases, creating synchronization lag and infrastructure overhead. To resolve this fragmentation, a single DynamoDB table can store operational document attributes alongside embeddings by leveraging native vector search. An Amazon Bedrock agent directs incoming queries to AWS Lambda action groups, which run standard CRUD operations or semantic searches against the DynamoDB SearchVectors API. When items change, a DynamoDB Streams pipeline uses Amazon Titan Text Embeddings V2 to generate new embeddings and write them back to the table. This unified approach eliminates standalone vector databases while keeping semantic indexes synchronized with operational updates.

## Context

AI agent architectures on AWS often store operational data in Amazon DynamoDB and vector embeddings in a separate dedicated vector store. This split increases infrastructure costs, adds synchronization complexity via dual writes, and introduces a risk of stale semantic retrieval results.

## Approach / What changed

The solution unifies operational and semantic storage in a single DynamoDB table utilizing native vector search. An Amazon Bedrock agent orchestrates user requests and invokes AWS Lambda action groups for standard CRUD queries or semantic similarity search via the SearchVectors API. An automated pipeline powered by DynamoDB Streams and AWS Lambda calls Amazon Titan Text Embeddings V2 to generate 1,024-dimension embeddings for new or updated records and write them back to the base table.

## Takeaways

- DynamoDB vector indexes require on-demand capacity mode, support up to five indexes per table with 4,096 dimensions each, and mandate equality-only SearchSchema HASH filtering.
- To prevent write loops during embedding generation, DynamoDB Streams must be configured with NEW\_AND\_OLD\_IMAGES so the pipeline Lambda can verify whether content actually changed.
- Standard DynamoDB condition keys such as dynamodb:LeadingKeys do not apply to SearchVectors, requiring multi-tenant workloads to isolate tenants via SearchSchema HASH keys or separate tables.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [AWS](https://yomu.fyi/topic/aws), [LLMs](https://yomu.fyi/topic/llm), [Search](https://yomu.fyi/topic/search), [Serverless](https://yomu.fyi/topic/serverless)

- Source: [AWS](https://aws.amazon.com/blogs/architecture/build-a-unified-ai-agent-architecture-with-dynamodb-and-bedrock)
- Source URL: https://aws.amazon.com/blogs/architecture/build-a-unified-ai-agent-architecture-with-dynamodb-and-bedrock
- Ingested by Yomu: 2026-08-27T15:08:46.849Z

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