Build enterprise search for agents with Amazon Bedrock Managed Knowledge Base

2026-07-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & & IT Infrastructure, Software Development & Engineering · Depth: Advanced, long

Summary

Amazon Bedrock now offers Managed Knowledge Base (MKB) in general availability, a fully managed agentic retrieval solution designed to simplify building enterprise search for agents and generative AI applications. MKB abstracts away the complexity of stitching together data ingestion, vector stores, and retrieval logic, handling scaling, high-accuracy retrieval, and document access control. It features native connectors for Amazon S3, Microsoft SharePoint, Atlassian Confluence, Google Drive, Microsoft OneDrive, and Web Crawler, supporting multimodal data parsing for files up to 10 GB. The service provides two retrieval APIs: "Retrieve" for direct lookups and "Agentic Retrieval" for complex, multi-hop queries that iteratively decompose and evaluate sub-queries. MKB also integrates with AgentCore Gateway for streamlined agent interaction and offers built-in observability via Amazon CloudWatch, ensuring production readiness. It is available in regions including us-east-1, us-west-2, and eu-west-1.

Key takeaway

For MLOps Engineers or AI Architects tasked with deploying scalable, secure RAG applications, Amazon Bedrock Managed Knowledge Base simplifies infrastructure management significantly. You can accelerate development by utilizing its fully managed data ingestion, multimodal parsing, and auto-scaling storage, freeing your team from provisioning vector stores or building custom retrieval pipelines. Consider integrating it with AgentCore Gateway to provide a unified, secure access point for your MCP-compatible agents, reducing operational complexity and ensuring production readiness from day one.

Key insights

Amazon Bedrock Managed Knowledge Base streamlines agentic RAG by fully managing infrastructure, data ingestion, and complex retrieval.

Principles

• Abstraction significantly reduces RAG operational overhead.
• Real-time ACL checks enhance document-level security.
• Iterative retrieval improves complex query accuracy.

Method

Set up a knowledge base by creating it, adding a data source like S3, and initiating ingestion. For advanced queries, Agentic Retrieval plans, executes sub-queries, evaluates results, and returns deduplicated chunks.

In practice

• Connect enterprise data via native connectors.
• Integrate with AgentCore Gateway for MCP agents.
• Monitor RAG performance using CloudWatch metrics.

Topics

• Amazon Bedrock
• Managed Knowledge Base
• Retrieval-Augmented Generation
• Agentic AI
• Enterprise Search
• AgentCore Gateway

Code references

• aws-samples/amazon-bedrock-samples

Best for: AI Engineer, MLOps Engineer, AI Architect

Related on AIssential

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.