Build Agentic Retrieval on Managed Amazon Bedrock Knowledge Base with CDK

Enterprise-grade Retrieval-Augmented Generation (RAG) has evolved far beyond simple vector search and standard similarity matching. Modern production applications demand sophisticated multi-source routing, rigorous compliance guarantees, comprehensive observability, and zero-touch infrastructure provisioning. When architecting complex generative AI workloads on Amazon Web Services, managing multiple data sources alongside multi-turn reasoning agents can quickly become an infrastructure nightmare without proper automation. This architectural deep-dive explores how to construct an enterprise-grade agentic retrieval pipeline utilizing a Managed Amazon Bedrock Knowledge Base orchestrated via the AWS Cloud Development Kit (CDK). By centralizing access patterns through a unified platform like n1n.ai, developers can further optimize downstream model inference costs and latency while focusing purely on business logic and routing semantics.

Architecture Overview: Multi-Source Agentic Routing

Traditional single-index RAG systems often fail when injected into complex enterprise domains containing disparate repositories such as legal contracts, internal engineering wikis, and financial balance sheets. A monolithic vector database frequently hallucinates or blends contexts inappropriately when forced to handle queries requiring distinct domain expertise.

To solve this, our target architecture implements an autonomous agent equipped with a semantic router. The agent evaluates an incoming user query, inspects the metadata profile, and dispatches the retrieval request to one of two specialized Amazon Bedrock Knowledge Bases. The routing layer sits downstream from an AgentCore Gateway, which acts as a secure, rate-limited perimeter enforcing strict IAM policies and payload sanitization before invoking foundation models such as Claude 3.5 Sonnet or DeepSeek-V3 via high-throughput endpoints provided by n1n.ai.

[User Query] --> [AgentCore Gateway] --> [Autonomous Router Agent]
                                                |
                      +-------------------------+-------------------------+
                      |                                                   |
                      v                                                   v
        [Knowledge Base A: Engineering]                     [Knowledge Base B: Legal/Finance]

Infrastructure as Code: Setting Up AWS CDK Constructs

Deploying stateful serverless AI pipelines requires absolute reproducibility. The AWS CDK enables us to define our Amazon Bedrock Knowledge Bases, associated OpenSearch Serverless collections, S3 data buckets, IAM execution roles, and Lambda orchestration functions inside a single, version-controlled Python application.

Below is a production-ready CDK stack snippet that provisions an encrypted S3 bucket, an OpenSearch Serverless vector collection, and links them directly to an Amazon Bedrock Knowledge Base resource with optimized chunking strategies.

from aws_cdk import (
    Duration,
    RemovalPolicy,
    Stack,
    aws_s3 as s3,
    aws_bedrock as bedrock,
    aws_iam as iam,
    aws_opensearchserverless as oss
)
from constructs import Construct

class BedrockAgenticRagStack(Stack):
    def __init__(self, scope: Construct, id: str, **kwargs) -> None:
        super().__init__(scope, id, **kwargs)

        # 1. S3 Data Source Bucket
        source_bucket = s3.Bucket(
            self, "KnowledgeBaseBucket",
            encryption=s3.BucketEncryption.S3_MANAGED,
            removal_policy=RemovalPolicy.DESTROY,
            auto_delete_objects=True
        )

        # 2. IAM Role for Bedrock Knowledge Base
        kb_role = iam.Role(
            self, "BedrockKBRole",
            assumed_by=iam.ServicePrincipal("bedrock.amazonaws.com")
        )
        source_bucket.grant_read(kb_role)

        # 3. OpenSearch Serverless Collection Vector Store
        # Note: Ensure encryption and network policies are attached in production
        vector_collection = oss.CfnCollection(
            self, "VectorCollection",
            name="enterprise-kb-store",
            type="VECTORSEARCH"
        )

        # 4. Amazon Bedrock Knowledge Base Resource
        knowledge_base = bedrock.CfnKnowledgeBase(
            self, "EnterpriseKnowledgeBase",
            name="EngineeringAndComplianceKB",
            role_arn=kb_role.role_arn,
            knowledge_base_configuration=bedrock.CfnKnowledgeBase.KnowledgeBaseConfigurationProperty(
                type="VECTOR",
                vector_knowledge_base_configuration=bedrock.CfnKnowledgeBase.VectorKnowledgeBaseConfigurationProperty(
                    embedding_model_arn=f"arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0"
                )
            ),
            storage_configuration=bedrock.CfnKnowledgeBase.StorageConfigurationProperty(
                type="OPENSEARCH_SERVERLESS",
                opensearch_serverless_configuration=bedrock.CfnKnowledgeBase.OpenSearchServerlessConfigurationProperty(
                    collection_arn=vector_collection.attr_arn,
                    vector_index_name="bedrock-knowledge-base-index",
                    field_mapping=bedrock.CfnKnowledgeBase.OpenSearchServerlessFieldMappingProperty(
                        vector_field="vector",
                        text_field="text",
                        metadata_field="metadata"
                    )
                )
            )
        )
        knowledge_base.add_dependency(vector_collection)

Implementing Agentic Routing and Gateway Integration

Once the foundational infrastructure is instantiated, the next critical phase involves writing the autonomous routing logic. When requests hit the AgentCore Gateway, they are intercepted by an AWS Lambda function running a LangChain or custom orchestration loop. This loop evaluates whether the prompt demands technical documentation from Knowledge Base A or compliance data from Knowledge Base B.

To maintain optimal performance and prevent rate-limit bottlenecks when scaling across thousands of concurrent enterprise users, integrating an efficient API gateway like n1n.ai guarantees high availability and intelligent load balancing across foundation model endpoints.

Here is a Python implementation snippet demonstrating how the routing agent interacts with Bedrock's retrieve-and-generate API, enriched with fallback handling:

import boto3
import json

bedrock_agent_runtime = boto3.client('bedrock-agent-runtime', region_name='us-east-1')

def evaluate_and_route_query(query_text: str, kb_id_engineering: str, kb_id_legal: str) -> dict:
    # Simple heuristic-based classifier or LLM router
    target_kb = kb_id_legal if any(keyword in query_text.lower() for keyword in ["compliance", "contract", "policy"]) else kb_id_engineering
    
    try:
        response = bedrock_agent_runtime.retrieve_and_generate(
            input={
                'text': query_text
            },
            retrieveAndGenerateConfiguration={
                'type': 'KNOWLEDGE_BASE',
                'knowledgeBaseConfiguration': {
                    'knowledgeBaseId': target_kb,
                    'modelArn': 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-5-sonnet-20241022-v2:0'
                }
            }
        }
        
        return {
            "status": "success",
            "output": response.get('output', {}).get('text'),
            "citations": response.get('citations', [])
        }
    except Exception as e:
        print(f"Error during agentic retrieval execution: {str(e)}")
        return {
            "status": "error",
            "message": str(e)
        }

Seven Layers of Observability and Evaluation

An enterprise AI application is incomplete without rigorous observability. Deploying our CDK architecture incorporates seven distinct layers of monitoring to ensure system health, safety, and output accuracy:

Infrastructure Metrics: CloudWatch metrics tracking CPU, memory, Lambda execution durations, and OpenSearch Serverless compute capacity units.
Gateway Latency Tracing: AWS X-Ray distributed tracing across the AgentCore Gateway and internal microservices.
Token Consumption & Cost Auditing: Granular tracking of prompt and completion tokens, complemented by cost-optimization insights.
Retrieval Relevance Scoring: Evaluating the cosine similarity scores and contextual precision of chunks returned by the Managed Knowledge Bases.
Model Hallucination Detection: Automated checks measuring output faithfulness against retrieved source citations.
Guardrail Policy Violations: Monitoring Amazon Bedrock Guardrails for toxic content, PII leakage, and prompt injection attempts.
Continuous Evaluation Pipelines: Scheduled evaluation runs using golden test datasets to measure regressions in retrieval accuracy over time.

Summary and Next Steps

Building an enterprise agentic retrieval pipeline with Managed Amazon Bedrock Knowledge Bases and AWS CDK eliminates the heavy lifting of maintaining custom vector indexes and manual chunking pipelines. By combining multi-source routing, robust infrastructure automation, and enterprise-grade tools like n1n.ai for optimized inference routing, engineering teams can rapidly deploy secure, cited, and highly scalable generative AI applications.

Get a free API key at n1n.ai

Source: https://aws.amazon.com/blogs/machine-learning/build-agentic-retrieval-on-a-managed-amazon-bedrock-knowledge-base-with-cdk/