Streamline AI Agents with DynamoDB and Bedrock Integration

Amazon DynamoDB just rolled out native vector search capabilities. Now, developers can store and query embeddings in the same table as their operational data. This update, which launched on August 5, 2026, aims to simplify the challenges teams face when building AI agents on AWS. Before, it was pretty common to see data fragmentation, with operational data in DynamoDB and vector embeddings managed elsewhere. This setup often led to higher infrastructure costs and more complex synchronization.

Now, with this capability, it's possible to design a unified AI agent system where a single DynamoDB table takes care of both structured data lookups and semantic similarity searches. The magic happens through the SearchVectors API operation, which integrates vector search directly into your established DynamoDB tables. This means developers can perform semantic searches right inside the DynamoDB environment, cutting down latency and the complexity involved in fetching data from different sources.

Why This Matters

Managing a production codebase often means juggling data across various storage solutions, which can crank up complexity and operational costs. Using just one data store for both operational data and vector embeddings can really streamline the architecture. This is a big win for applications using semantic search—think technical knowledge management platforms, where quick and accurate document retrieval is crucial.

Consolidating your data architecture can shave off infrastructure costs and reduce the chances of pulling stale data since everything sits in one DynamoDB table. The unified approach also makes the development process less of a headache because you don't have to worry about syncing separate data stores. Plus, having a single table design ensures efficient handling of both key-value lookups for operational data and approximate nearest neighbor (ANN) searches for semantic queries.

Implementing the Unified Architecture

You’ll need an AWS account with permissions to create DynamoDB tables, Lambda functions, and Bedrock agents to implement this architecture. The strategy involves a single-table design in DynamoDB that supports both key-value lookups and ANN search for semantic queries. A Bedrock agent manages user interactions and directs requests to the correct function within an action group Lambda.

The core components of this architecture are:

• A DynamoDB table with a vector index for storing documents, metadata, and 1,024-dimension embeddings.
• A Bedrock agent for orchestrating conversations and synthesizing responses.
• An action group Lambda for executing semantic searches and performing CRUD operations.
• An embedding pipeline Lambda, triggered by DynamoDB Streams, to generate embeddings for new or updated content.

Designing the Single-Table Schema

The table utilizes a composite primary key, with entity_id as the partition key and sk as the sort key, storing embeddings as a list of numbers. The vector index organizes search results by the category attribute. It’s crucial to pick a partition key that’s just right in terms of cardinality to ensure efficient throughput scaling. A key with too low cardinality can bunch data into a handful of partitions, which can throttle throughput. Meanwhile, a key that's unique per item might not have neighbors for comparison, affecting search efficiency.

For workloads involving multiple tenants, using tenant_id as the partition key is generally a good call. Here’s a quick AWS CLI command to set up the vector index on an existing table:

aws dynamodb update-table --table-name YourTableName \
    --attribute-definitions AttributeName=category,AttributeType=S \
    --global-secondary-index-updates \
        "[{\"Create\":{\"IndexName\":\"YourIndexName\",\"KeySchema\":[{\"AttributeName\":\"category\",\"KeyType\":\"HASH\"}],\"Projection\":{\"ProjectionType\":\"ALL\"}}}]"

Once the index is created, you'll need to wait for it to become searchable. Do this by polling DescribeTable until the IndexStatus shows as ACTIVE and Backfilling isn’t true anymore. Be aware that the first few searches after the index goes ACTIVE might still throw ValidationException errors due to the dedicated search endpoint. Treat these as retryable, not outright failures.

Building the Action Group Lambda

The action group Lambda is key for handling both semantic searches and operational lookups. It involves generating a query embedding using Amazon Titan Text Embeddings V2 and hitting the SearchVectors API. The index uses COSINE distance, where lower scores suggest higher similarity, so naming the field correctly is vital to prevent the agent from flipping the ranking.

The Lambda handler directs requests based on the function name specified by the Bedrock agent. Here’s a basic example of how you'd set up the Lambda handler:

def lambda_handler(event, context):
    function_name = event['function_name']
    if function_name == 'semantic_search':
        return handle_semantic_search(event)
    elif function_name == 'crud_operation':
        return handle_crud_operation(event)
    else:
        raise ValueError('Unknown function name')

Automating Embeddings with DynamoDB Streams

The embedding pipeline Lambda gets triggered by INSERT and MODIFY events in DynamoDB Streams. It creates embeddings for new or altered content and writes them back to the same item. An infinite-loop guard checks the content field between old and new images to avoid constant triggering. This guard requires StreamViewType = NEW_AND_OLD_IMAGES to make sure the OldImage is available for comparison.

For production environments, set up the event source mapping with ReportBatchItemFailures to effectively manage failed records. It’s also a good idea to use an Amazon SQS dead-letter queue for records that keep failing. Implement retries with exponential backoff for Amazon Bedrock InvokeModel calls to deal with throttling. This makes sure only failed records are retried, keeping the system steady under load.

Limitations and Considerations

This unified architecture simplifies data management but has its limits. DynamoDB vector indexes need to use on-demand capacity mode and allow a maximum of five vector indexes per table, each supporting up to 4,096 dimensions. The SearchVectors API has its constraints, like a 16 MB response limit and no support for pagination. It’s smart to project only the attributes you need and keep the TopK small enough to fit within these constraints.

Apps that need advanced search features, such as range filters or aggregations, might still need the capabilities of Amazon OpenSearch Service. Moreover, although this architecture reduces complexity, it's most advantageous for apps already using DynamoDB as their main operational store. If your data is stored in Amazon S3 or you need more than equality-based filtering, alternatives like Amazon Bedrock Knowledge Bases or Amazon OpenSearch Service could be a better fit.

What I'd Do on Monday

If you’re dealing with a fragmented data architecture, it could be worth evaluating if this unified approach is right for your application. Start by looking at your current infrastructure costs and challenges with data synchronization. Then, develop a migration plan to consolidate your data into one DynamoDB table. This means figuring out your current data models and the changes needed to fit the new unified architecture.

Make sure your team has the necessary AWS resources and permissions. Get acquainted with the DynamoDB vector search documentation to grasp the best practices and limitations. Finally, set up a prototype in a test environment to see if it works for your specific case. By taking these steps, you can streamline your AI agent architecture, cut down on operational overhead, and improve data consistency—all while leveraging Amazon's solid cloud infrastructure.