Today, we’re announcing Amazon Bedrock Managed Knowledge Base , a new set of capabilities that enables developers to build enterprise-grade generative AI applications with their proprietary data in minutes. Organizations building agentic AI applications need secure, reliable, and up-to-date access to enterprise-wide data to deliver accurate, fast, and trusted outcomes.
Managed Knowledge Base abstracts away the complexity of building and managing retrieval-augmented generation (RAG) pipelines, allowing developers to focus on business outcomes rather than infrastructure management. Developers building knowledge bases for their agents face three key challenges today: Connecting to enterprise data – Enterprise knowledge lives across disparate systems with different content types, access control lists, and document formats.
Building and maintaining custom connectors for each source adds complexity that slows down development. Optimizing RAG accuracy – Best practices for retrieval-augmented generation keep evolving. Developers need to experiment with different parsing strategies, chunking approaches, embedding models, and agentic retrieval behaviors to get accurate answers from their data.
Managing infrastructure at scale – Organizations need to serve large knowledge bases with millions of documents, or manage thousands of smaller knowledge bases across teams. Both patterns require reliable infrastructure, security enforcement, and cost control. These challenges require developers to repeatedly perform undifferentiated work instead of focusing on their applications.
Amazon Bedrock Managed Knowledge Base addresses these challenges by abstracting away the multiple infrastructure components developers traditionally have to assemble and maintain themselves (storage, retrieval, embeddings, re-ranking, and foundation model selection) into a single managed primitive. By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself.
On top of this managed foundation, three core innovations further improve ease of use and accuracy: Native data connectors – Six pre-built ingestion connectors that natively pull enterprise data and permissions from SaaS applications, eliminating the overhead developers face in managing application-specific requirements. At launch, we support Amazon S3, SharePoint, Confluence, Web Crawler, Google Drive, and OneDrive.
Smart Parsing – Different content types and sources require different approaches to achieve accurate retrieval. Smart Parsing handles this complexity automatically, selecting the right parsing strategy for each data type and connector to provide the highest accuracy for your agents. Agentic Retriever – Optimized for complex queries that require multiturn, multihop retrieval within a single knowledge base or across multiple knowledge bases.
Agentic Retriever automatically infers end-user intent and draws relevant context from institutional knowledge spread across data sources and modalities. With just a few lines of code, Amazon Bedrock Managed Knowledge Base automatically manages and scales the end-to-end RAG pipeline that powers your enterprise knowledge agents. For agent builders, it’s available as a pre-built target type in Amazon Bedrock AgentCore Gateway , reducing integration to a few lines of code, auto-generating role-based permissions, and providing observability and evaluation metrics in the AgentCore Observability dashboard.
Getting started with Amazon Bedrock Managed Knowledge Base Creating a Managed Knowledge Base is straightforward. Navigate to the Amazon Bedrock AgentCore console or the Amazon Bedrock console , open the Knowledge Bases page, and choose Create Managed KB . The experience is the same in both consoles.
You will see that Unstructured Vector Store KB is now available as the recommended option, alongside the other knowledge base types you may already be familiar with: Picture 1 – Knowledge Bases list page in the Amazon Bedrock AgentCore console showing the Type column with different KB types and the Create Managed KB button When creating a new Knowledge Bases, you can connect to your enterprise data sources by choosing from the list of supported connectors directly from a dropdown.
AWS Identity and Access Management (IAM) roles are automatically created, and you can choose to edit these permissions if needed: Picture 2 – Create Knowledge Base page showing the Data source dropdown expanded with all supported connectors: Amazon S3, Confluence, Custom, Google Drive, One Drive, SharePoint, and Web Crawler An optimized set of defaults will be presented, allowing you to create your knowledge base in just a few clicks.
Once the data is synced, you can integrate the knowledge base with your agent or provide it as a tool for your foundation model and start querying. Smart Parsing for accurate data ingestion One of the key challenges in building knowledge bases is preparing diverse data types for accurate retrieval. Once you point Managed Knowledge Base at your data sources, Smart Parsing automatically determines the optimal parsing strategy for each data type and connector, no extra configuration is required.
Smart Parsing combines multiple techniques: Connector-specific data models – Optimized handling for each data source. For example, the Web Crawler connector preserves HTML structure including embedded images and tables, ensuring rich content is not dropped during ingestion. SharePoint connectors maintain document hierarchy and relationships between files.
Multimodal processing – Automatic detection and processing of different content types within documents. The system identifies bounding boxes in documents, then sends them to foundation models for data extraction, captioning, and scene description in video files.
Originally published at aws.amazon.com
