AWS shows how to build enterprise agentic retrieval on Bedrock with CDK
AWS ML Blog · rss · 2026-10-27
AWS ML Blog walks through building enterprise agentic retrieval on a Managed Amazon Bedrock Knowledge Base using AWS CDK.
Why classic RAG breaks
Single-shot embed-and-fetch RAG returns fluent but incomplete answers for questions spanning multiple corpora, questions where the right source depends on the query, and questions needing a second retrieval pass.
New architecture
- Amazon Bedrock now offers a Managed Knowledge Base (Type: MANAGED) that handles ingestion, storage, indexing, and retrieval with built-in embedding and reranking — no OpenSearch Serverless collection or OCU capacity to provision
- Amazon Bedrock AgentCore provides a Gateway exposing each knowledge base as an MCP tool, a Runtime hosting the reasoning agent, and the AgenticRetrieveStream API (multi-turn planning, internal reranking, cited answers)
- Agentic retrieval is only available on the managed knowledge base; DIY remains the choice when you need your own vector store, data residency, or compliance control
Observability and evaluation
An agentic loop takes a different path per question, so observability is essential. One CDK deploy provisions two CloudWatch dashboards across seven layers of telemetry plus on-demand and continuous evaluation, with grounded citations and faithfulness/correctness scores to catch answer drift. The whole system ships as a single Python CDK app.
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