MDAA TS Docs
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    interface BedrockBuilderL3ConstructProps {
        agentBucketArn?: string;
        agents?: NamedAgentProps;
        createOutputs?: boolean;
        createParams?: boolean;
        crossAccountStacks?: { [account: string]: { [region: string]: Stack } };
        dataAdminRoles: MdaaRoleRef[];
        guardrails?: NamedGuardrailProps;
        kmsKeyArn?: string;
        knowledgeBases?: NamedKnowledgeBaseProps;
        lambdaFunctions?: LambdaFunctionProps;
        naming: IMdaaResourceNaming;
        roleHelper: MdaaRoleHelper;
        tags?: { [key: string]: string };
        vectorStores?: NamedVectorStoreProps;
    }

    Hierarchy

    • MdaaL3ConstructProps
      • BedrockBuilderL3ConstructProps
    Index

    Properties

    agentBucketArn?: string

    Existing S3 bucket ARN for agent data storage. If omitted, a dedicated bucket is created automatically.

    Use cases: Agent artifact storage, data management, bucket reuse

    AWS: S3 bucket for Bedrock agent storage

    Validation: Optional; String; must be valid S3 bucket ARN

    Bedrock agent configurations with foundation models, action groups, knowledge base integration, and guardrails.

    Use cases: AI agent deployment, conversational AI, intelligent automation

    AWS: Amazon Bedrock Agents

    Validation: Optional; NamedAgentProps (map of agent name to config)

    createOutputs?: boolean

    Flag controlling CloudFormation output and stack export creation for construct resources

    createParams?: boolean

    Flag controlling SSM parameter creation for construct resource references enabling

    crossAccountStacks?: { [account: string]: { [region: string]: Stack } }
    dataAdminRoles: MdaaRoleRef[]

    Admin roles granted access to Bedrock agent resources including KMS keys and S3 buckets.

    Use cases: Administrative access, resource management, security control

    AWS: IAM roles for Bedrock resource administration

    Validation: Required; MdaaRoleRef[]

    guardrails?: NamedGuardrailProps

    Guardrail configurations for AI safety, content filtering, and responsible AI deployment.

    Use cases: AI safety controls, content filtering, responsible AI, content moderation

    AWS: Bedrock Guardrails

    Validation: Optional; NamedGuardrailProps (map of guardrail name to config)

    kmsKeyArn?: string

    Existing KMS key ARN for encrypting Bedrock agent resources. If omitted, a customer-managed key is created automatically.

    Use cases: Customer-controlled encryption, security compliance, key reuse

    AWS: KMS key for Bedrock resource encryption

    Validation: Optional; String; must be valid KMS key ARN

    knowledgeBases?: NamedKnowledgeBaseProps

    Knowledge base configurations with S3/SharePoint data sources and custom parsing strategies. Enables document ingestion, embedding generation, and retrieval for RAG applications.

    Use cases: Knowledge management, document processing, question-answering, RAG

    AWS: Bedrock Knowledge Bases

    Validation: Optional; NamedKnowledgeBaseProps (map of KB name to config)

    lambdaFunctions?: LambdaFunctionProps

    Lambda functions and layers for Bedrock agent action groups. Enables custom business logic and external API integrations.

    Use cases: Custom action group logic, external integrations, function deployment

    AWS: Lambda functions/layers for Bedrock agent action groups

    Validation: Optional; LambdaFunctionProps

    naming: IMdaaResourceNaming

    MDAA naming implementation for consistent resource naming across all MDAA constructs

    roleHelper: MdaaRoleHelper
    tags?: { [key: string]: string }
    vectorStores?: NamedVectorStoreProps

    Vector store configurations for knowledge bases (OpenSearch Serverless or Aurora). Provides vector database storage for semantic search and RAG.

    Use cases: Semantic search, RAG applications, knowledge retrieval, embedding storage

    AWS: OpenSearch Serverless or Aurora vector stores

    Validation: Optional; NamedVectorStoreProps (map of store name to config)