AWS Details Multi-Tenant and Observable Agentic Retrieval with Amazon Bedrock

Multi-tenant agentic chat assistants are a frequent request from large-scale customers, particularly for document chat. [1] The conversational interface is straightforward, but the underlying multi-tenant agentic retrieval system requires isolating each tenant's documents using a verified identity. [1] Amazon Bedrock Managed Knowledge Base alleviates this undifferentiated work by managing ingestion, storage, embedding, and ranking. [1] It provides built-in agentic retrieval that uses iterative planning and multi-hop retrievals to answer complex questions while honoring access permissions on every hop. [1]

Enterprise agentic retrieval allows an agent to reason about a question, route it to the correct knowledge base, retrieve iteratively, and return a cited answer. [2] However, once an agent reasons and retrieves in a loop, it becomes difficult to see what it did or evaluate the answer's quality. [2] A new AWS solution uses Amazon Bedrock Managed Knowledge Base and Amazon Bedrock AgentCore to build an enterprise agentic retrieval system with observability and evaluation built in from the start. [2] The entire architecture can be deployed with a single AWS CloudFormation chain. [2]

These architectures demonstrate AWS's push to simplify the operational burden of deploying complex generative AI systems in production. By moving from single-shot Retrieval Augmented Generation (RAG) to managed agentic retrieval, developers no longer need to manually orchestrate multi-hop reasoning or vector infrastructure. This compares favorably to self-managed vector stores, which require significant overhead to ensure multi-tenant security and observability. What the sources don't address: How the latency and cost of multi-hop managed agentic retrieval scale compared to traditional single-shot RAG pipelines.