AWS CloudFormation Amazon Bedrock Skill

AWS CloudFormation Amazon Bedrock

Overview

Creates production-ready AI infrastructure using AWS CloudFormation templates for Amazon Bedrock. Covers Bedrock agents, knowledge bases for RAG implementations, data source connectors, guardrails for content moderation, prompt management, workflow orchestration with flows, and inference profiles for optimized model access.

When to Use

Creating Bedrock agents with action groups
Implementing RAG with knowledge bases
Configuring S3 or web crawl data sources
Setting up content moderation guardrails
Managing prompt templates
Orchestrating AI workflows with Bedrock Flows
Configuring inference profiles for multi-model access
Organizing templates with Parameters and cross-stack references

Instructions

1. Define Parameters

Parameters:
  FoundationModel:
    Type: String
    Default: anthropic.claude-3-sonnet-20240229-v1:0
    AllowedValues:
      - anthropic.claude-3-sonnet-20240229-v1:0
      - anthropic.claude-3-haiku-20240307-v1:0
      - amazon.titan-text-express-v1
    Description: Foundation model for agent

2. Create Agent Role

Resources:
  AgentRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: BedrockPermissions
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - bedrock:InvokeModel
                Resource: !Sub "arn:aws:bedrock:${AWS::Region}:${AWS::AccountId}:foundation-model/${FoundationModel}"

3. Create Agent

  BedrockAgent:
    Type: AWS::Bedrock::Agent
    Properties:
      AgentName: !Sub "${AWS::StackName}-agent"
      AgentResourceRoleArn: !GetAtt AgentRole.Arn
      FoundationModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::foundation-model/${FoundationModel}"
      AutoPrepare: true
      Instruction: |
        You are a helpful assistant. Use the knowledge base to answer questions.

4. Create Knowledge Base

  KnowledgeBaseRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole

  KnowledgeBase:
    Type: AWS::Bedrock::KnowledgeBase
    Properties:
      Name: !Sub "${AWS::StackName}-kb"
      RoleArn: !GetAtt KnowledgeBaseRole.Arn
      KnowledgeBaseConfiguration:
        Type: VECTOR
        VectorKnowledgeBaseConfiguration:
          EmbeddingModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::embedding-model/amazon.titan-embed-text-v1"

5. Create Data Source

  DataBucket:
    Type: AWS::S3::Bucket

  S3DataSource:
    Type: AWS::Bedrock::DataSource
    Properties:
      KnowledgeBaseId: !Ref KnowledgeBase
      Name: s3-data-source
      Type: S3
      DataSourceConfiguration:
        S3Configuration:
          BucketArn: !GetAtt DataBucket.Arn
          InclusionPrefixes:
            - documents/

6. Add Guardrail

  Guardrail:
    Type: AWS::Bedrock::Guardrail
    Properties:
      Name: !Sub "${AWS::StackName}-guardrail"
      BlockedInputMessaging: "I cannot help with that request."
      ContentPolicyConfig:
        filtersConfig:
          - type: PROFANITY
          - type: MISCONDUCT

7. Create Action Group

  ActionLambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      Runtime: python3.12
      Handler: index.handler
      Role: !GetAtt ActionLambdaRole.Arn
      Code:
        ZipFile: |
          def handler(event, context):
              return {"statusCode": 200, "body": "{\"result\": \"success\"}"}

  ActionGroup:
    Type: AWS::Bedrock::AgentActionGroup
    Properties:
      ActionGroupName: api-operations
      ActionGroupState: ENABLED
      AgentId: !GetAtt BedrockAgent.AgentId
      ActionGroupExecutor:
        Lambda: !Ref ActionLambdaFunction
      FunctionSchema:
        functionConfigurations:
          - function: |
              { "name": "get_inventory", "description": "Get current inventory status", "parameters": { "type": "object", "properties": { "sku": { "type": "string" } }, "required": [] } }

8. Validate Before Deploy

Always validate the template before deployment:

aws cloudformation validate-template --template-body file://bedrock-template.yaml

9. Verify After Deploy

# Check agent status
aws bedrock-agent get-agent --agent-id $(aws cloudformation describe-stacks --stack-name STACK_NAME --query 'Stacks[0].Outputs[?OutputKey==`AgentId`].OutputValue' --output text)

# Check knowledge base sync status
aws bedrock-agent list-knowledge-bases --agent-id AGENT_ID

# Test guardrail
aws bedrock-runtime apply_guardrail --guardrail-identifier GUARDRAIL_ID --source SOURCE

Examples

Minimal RAG Agent Template

Complete working template for a RAG-enabled agent:

AWSTemplateFormatVersion: "2010-09-09"
Description: "Bedrock RAG Agent with Knowledge Base"

Parameters:
  FoundationModel:
    Type: String
    Default: anthropic.claude-3-sonnet-20240229-v1:0

Resources:
  # IAM Role for Agent
  AgentRole:
    Type: AWS::IAM::Role
    Properties:
      RoleName: !Sub "${AWS::StackName}-agent-role"
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: InvokeModel
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action: bedrock:InvokeModel
                Resource: "*"

  # IAM Role for Knowledge Base
  KnowledgeBaseRole:
    Type: AWS::IAM::Role
    Properties:
      RoleName: !Sub "${AWS::StackName}-kb-role"
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: S3Access
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action: s3:GetObject
                Resource: !Sub "${DataBucket.Arn}/*"

  # S3 Bucket for Documents
  DataBucket:
    Type: AWS::S3::Bucket

  # Knowledge Base
  KnowledgeBase:
    Type: AWS::Bedrock::KnowledgeBase
    Properties:
      Name: !Sub "${AWS::StackName}-kb"
      RoleArn: !GetAtt KnowledgeBaseRole.Arn
      KnowledgeBaseConfiguration:
        Type: VECTOR
        VectorKnowledgeBaseConfiguration:
          EmbeddingModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::embedding-model/amazon.titan-embed-text-v1"

  # Data Source
  DataSource:
    Type: AWS::Bedrock::DataSource
    Properties:
      KnowledgeBaseId: !Ref KnowledgeBase
      Name: !Sub "${AWS::StackName}-ds"
      Type: S3
      DataSourceConfiguration:
        S3Configuration:
          BucketArn: !GetAtt DataBucket.Arn

  # Bedrock Agent
  BedrockAgent:
    Type: AWS::Bedrock::Agent
    Properties:
      AgentName: !Sub "${AWS::StackName}-agent"
      AgentResourceRoleArn: !GetAtt AgentRole.Arn
      FoundationModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::foundation-model/${FoundationModel}"
      AutoPrepare: true
      Instruction: |
        You are a helpful assistant. Use the knowledge base to answer user questions accurately.

Outputs:
  AgentId:
    Description: Bedrock Agent ID
    Value: !GetAtt BedrockAgent.AgentId
  KnowledgeBaseId:
    Description: Knowledge Base ID
    Value: !Ref KnowledgeBase

Guardrail with Content Filtering

Resources:
  Guardrail:
    Type: AWS::Bedrock::Guardrail
    Properties:
      Name: !Sub "${AWS::StackName}-guardrail"
      blockedInputMessaging: "Content blocked by safety filters."
      blockedOutputMessaging: "Response filtered for safety."
      contentPolicyConfig:
        filtersConfig:
          - type: PROFANITY
            inputStrength: HIGH
            outputStrength: HIGH
          - type: MISCONDUCT
            inputStrength: HIGH
            outputStrength: HIGH
      sensitiveInformationPolicyConfig:
        piiEntitiesConfig:
          - type: EMAIL
            action: ANONYMIZE
          - type: SSN
            action: BLOCK

Best Practices

Security

Use least privilege IAM policies for agent and knowledge base roles
Restrict web crawl data sources to trusted internal domains
Encrypt sensitive data in knowledge bases
Parameterize all TemplateURL values for nested stacks

Cost Optimization

Select appropriate model size for task complexity
Configure retrieval filtering to reduce token usage
Set chunk size limits to control storage costs
Monitor usage with CloudWatch dashboards

Performance

Optimize chunk size for embedding quality
Use provisioned throughput for high-traffic vector stores
Configure appropriate knowledge base sync intervals
Implement caching for frequently accessed content

Validation

Always run aws cloudformation validate-template before deploy
Verify agent status after stack creation completes
Test guardrails with sample inputs
Monitor knowledge base sync status in CloudWatch

Constraints and Warnings

For detailed limits, see constraints.md:

Regional limits: Not all models available in all regions
Agent initialization: AutoPrepare may take several minutes
Knowledge base sync: S3 sync is near-instant; web crawl takes longer
Web crawl security: Always restrict to trusted domains to prevent prompt injection
Token limits: Configure MaxTokens parameter for your use case
Quota management: Request quota increases via AWS Support if needed

Security

Restrict web crawl data sources to trusted internal domains only
Validate content before ingesting into knowledge bases
Use parameterized TemplateURL values for nested stacks
Implement guardrails for content moderation
Apply least privilege IAM policies to agent roles
Encrypt sensitive data in knowledge bases
Monitor for prompt injection in web-crawled content

Cost Optimization

Use appropriate model selection for task complexity
Implement knowledge base retrieval filtering
Set chunk size limits to control token usage
Monitor token consumption with CloudWatch
Use auto-prepare agents strategically
Implement batch processing for non-real-time workloads
Use knowledge base filtering to reduce costs

Performance

Optimize chunk size for embedding quality vs. cost
Use vector store optimization (OpenSearch, Pinecone)
Implement caching for frequently accessed knowledge base content
Configure appropriate knowledge base sync intervals
Use provisioned throughput for vector databases
Monitor agent initialization and cold start times
Implement graceful degradation for rate limiting

Data Management

Use appropriate inclusion/exclusion filters for data sources
Implement document validation before indexing
Use versioning for knowledge base updates
Configure appropriate sync intervals for data sources
Implement content deduplication in knowledge bases
Use metadata filtering for improved retrieval accuracy
Monitor knowledge base size and document limits

References

constraints.md - Resource limits, regional constraints, operational limits, and cost considerations
reference.md - API reference and resource properties
examples.md - Additional usage examples

Agent Skills: AWS CloudFormation Amazon Bedrock

Install this agent skill to your local

Skill Files

AWS CloudFormation Amazon Bedrock

Overview

When to Use

Instructions

1. Define Parameters

2. Create Agent Role

3. Create Agent

4. Create Knowledge Base

5. Create Data Source

6. Add Guardrail

7. Create Action Group

8. Validate Before Deploy

9. Verify After Deploy

Examples

Minimal RAG Agent Template

Guardrail with Content Filtering

Best Practices

Security

Cost Optimization

Performance

Validation

Constraints and Warnings

Security

Cost Optimization

Performance

Data Management

References

Related Resources