Source time Edition date
IndustryAWS Machine Learning Blog
Build agentic retrieval on a Managed Amazon Bedrock Knowledge Base with CDK
使用 CDK 在 Managed Amazon Bedrock Knowledge Base 上构建代理式检索
Worth reading to judge whether Amazon Bedrock's seven-layer observability and dual evaluation modes justify the complexity teams take on by deploying agentic retrieval as one CDK application.
Judgment
Platform and ML teams deciding whether to replace single-pass RAG should adopt the CDK agentic-retrieval stack only when queries span multiple corpora, require source routing, or need a second retrieval pass; otherwise the added Bedrock and CDK complexity is not justified.
Basis
AWS's Prabhakar Chandrasekaran and Siddhant Sahu describe building enterprise agentic retrieval on a Managed Amazon Bedrock Knowledge Base using the AWS CDK. They contrast it with ordinary RAG: embed the question, fetch nearest chunks from one index, hand them to a model, and return one answer. That pattern is fine for many questions but fails when an answer spans two corpora (single retrieval returns half, yielding fluent but incomplete answers), when the right source depends on the question (a single index cannot choose), or when a second look at the corpus is needed after the first result (there is nowhere for that second pass). The CDK application is the proposed remedy, but the excerpt stops before detailing the implementation.
Unclear The source does not quantify the latency, cost, or operational burden of the CDK deployment compared with single-pass RAG.
Next Verify the CDK stack's evaluation and observability modules on a two-corpus test query before approving a production migration.