Amazon Bedrock AgentCore Construct Library

The APIs of higher level constructs in this module are experimental and under active development. They are subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model and breaking changes will be announced in the release notes. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

| Language | Package | | :--------------------------------------------------------------------------------------------- | --------------------------------------- | | TypeScript | @aws-cdk/aws-bedrock-agentcore-alpha |

Amazon Bedrock AgentCore enables you to deploy and operate highly capable AI agents securely, at scale. It offers infrastructure purpose-built for dynamic agent workloads, powerful tools to enhance agents, and essential controls for real-world deployment. AgentCore services can be used together or independently and work with any framework including CrewAI, LangGraph, LlamaIndex, and Strands Agents, as well as any foundation model in or outside of Amazon Bedrock, giving you ultimate flexibility. AgentCore eliminates the undifferentiated heavy lifting of building specialized agent infrastructure, so you can accelerate agents to production.

This construct library facilitates the deployment of Bedrock AgentCore primitives, enabling you to create sophisticated AI applications that can interact with your systems and data sources.

Note: Users need to ensure their CDK deployment role has the iam:CreateServiceLinkedRole permission for AgentCore service-linked roles.

Table of contents

• AgentCore Runtime
  • Runtime Versioning
  • Runtime Endpoints
  • AgentCore Runtime Properties
  • Runtime Endpoint Properties
  • Creating a Runtime
    • Option 1: Use an existing image in ECR
    • Managing Endpoints and Versions
    • Option 2: Use a local asset
• Browser Custom tool
  • Browser properties
  • Browser Network modes
  • Basic Browser Creation
  • Browser IAM permissions
• Code Interpreter Custom tool
  • Code Interpreter properties
  • Code Interpreter Network Modes
  • Basic Code Interpreter Creation
  • Code Interpreter IAM permissions
• Gateway
  • Gateway Properties
  • Basic Gateway Creation
  • Protocol configuration
  • Inbound authorization
  • Gateway with KMS Encryption
  • Gateway with Custom Execution Role
  • Gateway IAM Permissions
• Gateway Target
  • Gateway Target Properties
  • Targets types
  • Outbound auth
  • Api schema
  • Basic Gateway Target Creation
    • Using addTarget methods (Recommended)
    • Using static factory methods
  • Lambda Target with Tool Schema
  • Smithy Model Target with OAuth
  • Gateway Target IAM Permissions
• Memory
  • Memory properties
  • Basic Memory Creation
  • LTM Memory Extraction Stategies
  • Memory Strategy Methods
• Amazon Bedrock AgentCore Construct Library
  • Table of contents
  • AgentCore Runtime
    • Runtime Endpoints
    • AgentCore Runtime Properties
    • Runtime Endpoint Properties
    • Creating a Runtime
      • Option 1: Use an existing image in ECR
      • Option 2: Use a local asset
      • Option 3: Use direct code deployment
    • Granting Permissions to Invoke Bedrock Models or Inference Profiles
    • Runtime Versioning
      • Managing Endpoints and Versions
        • Step 1: Initial Deployment
        • Step 2: Creating Custom Endpoints
        • Step 3: Runtime Update Deployment
        • Step 4: Testing with Staging Endpoints
        • Step 5: Promoting to Production
    • Creating Standalone Runtime Endpoints
      • Example: Creating an endpoint for an existing runtime
    • Runtime Authentication Configuration
      • IAM Authentication (Default)
      • Cognito Authentication
      • JWT Authentication
      • OAuth Authentication
      • Using a Custom IAM Role
    • Runtime Network Configuration
      • Public Network Mode (Default)
      • VPC Network Mode
      • Managing Security Groups with VPC Configuration
  • Browser
    • Browser Network modes
    • Browser Properties
    • Basic Browser Creation
    • Browser with Tags
    • Browser with VPC
    • Browser with Recording Configuration
    • Browser with Custom Execution Role
    • Browser with S3 Recording and Permissions
    • Browser IAM Permissions
  • Code Interpreter
    • Code Interpreter Network Modes
    • Code Interpreter Properties
    • Basic Code Interpreter Creation
    • Code Interpreter with VPC
    • Code Interpreter with Sandbox Network Mode
    • Code Interpreter with Custom Execution Role
    • Code Interpreter IAM Permissions
    • Code interpreter with tags
  • Memory
    • Memory Properties
    • Basic Memory Creation
    • LTM Memory Extraction Stategies
    • Memory with Built-in Strategies
    • Memory with custom Strategies
      • Memory with Custom Execution Role
    • Memory with self-managed Strategies
    • Memory Strategy Methods

AgentCore Runtime

The AgentCore Runtime construct enables you to deploy containerized agents on Amazon Bedrock AgentCore. This L2 construct simplifies runtime creation just pass your ECR repository name and the construct handles all the configuration with sensible defaults.

Runtime Endpoints

Endpoints provide a stable way to invoke specific versions of your agent runtime, enabling controlled deployments across different environments. When you create an agent runtime, Amazon Bedrock AgentCore automatically creates a "DEFAULT" endpoint which always points to the latest version of runtime.

You can create additional endpoints in two ways:

1. Using Runtime.addEndpoint() - Convenient method when creating endpoints alongside the runtime.
2. Using RuntimeEndpoint - Flexible approach for existing runtimes.

For example, you might keep a "production" endpoint on a stable version while testing newer versions through a "staging" endpoint. This separation allows you to test changes thoroughly before promoting them to production by simply updating the endpoint to point to the newer version.

AgentCore Runtime Properties

| Name | Type | Required | Description | |------|------|----------|-------------| | runtimeName | string | Yes | The name of the agent runtime. Valid characters are a-z, A-Z, 0-9, _ (underscore). Must start with a letter and can be up to 48 characters long | | agentRuntimeArtifact | AgentRuntimeArtifact | Yes | The artifact configuration for the agent runtime containing the container configuration with ECR URI | | executionRole | iam.IRole | No | The IAM role that provides permissions for the agent runtime. If not provided, a role will be created automatically | | networkConfiguration | NetworkConfiguration | No | Network configuration for the agent runtime. Defaults to RuntimeNetworkConfiguration.usingPublicNetwork() | | description | string | No | Optional description for the agent runtime | | protocolConfiguration | ProtocolType | No | Protocol configuration for the agent runtime. Defaults to ProtocolType.HTTP | | authorizerConfiguration | RuntimeAuthorizerConfiguration | No | Authorizer configuration for the agent runtime. Use RuntimeAuthorizerConfiguration static methods to create configurations for IAM, Cognito, JWT, or OAuth authentication | | environmentVariables | { [key: string]: string } | No | Environment variables for the agent runtime. Maximum 50 environment variables | | tags | { [key: string]: string } | No | Tags for the agent runtime. A list of key:value pairs of tags to apply to this Runtime resource | | lifecycleConfiguration | LifecycleConfiguration | No | The life cycle configuration for the AgentCore Runtime. Defaults to 900 seconds (15 minutes) for idle, 28800 seconds (8 hours) for max life time | | requestHeaderConfiguration | RequestHeaderConfiguration | No | Configuration for HTTP request headers that will be passed through to the runtime. Defaults to no configuration |

Runtime Endpoint Properties

| Name | Type | Required | Description | |------|------|----------|-------------| | endpointName | string | Yes | The name of the runtime endpoint. Valid characters are a-z, A-Z, 0-9, _ (underscore). Must start with a letter and can be up to 48 characters long | | agentRuntimeId | string | Yes | The Agent Runtime ID for this endpoint | | agentRuntimeVersion | string | Yes | The Agent Runtime version for this endpoint. Must be between 1 and 5 characters long.| | description | string | No | Optional description for the runtime endpoint | | tags | { [key: string]: string } | No | Tags for the runtime endpoint |

Creating a Runtime

Option 1: Use an existing image in ECR

Reference an image available within ECR.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

// The runtime by default create ECR permission only for the repository available in the account the stack is being deployed
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

// Create runtime using the built image
const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact
});

Option 2: Use a local asset

Reference a local directory containing a Dockerfile. Images are built from a local Docker context directory (with a Dockerfile), uploaded to Amazon Elastic Container Registry (ECR) by the CDK toolkit,and can be naturally referenced in your CDK app.

const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromAsset(
  path.join(__dirname, "path to agent dockerfile directory")
);

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
});

Option 3: Use direct code deployment

With the container deployment method, developers create a Dockerfile, build ARM-compatible containers, manage ECR repositories, and upload containers for code changes. This works well where container DevOps pipelines have already been established to automate deployments.

However, customers looking for fully managed deployments can benefit from direct code deployment, which can significantly improve developer time and productivity. Direct code deployment provides a secure and scalable path forward for rapid prototyping agent capabilities to deploying production workloads at scale.

With direct code deployment, developers create a zip archive of code and dependencies, upload to Amazon S3, and configure the bucket in the agent configuration. A ZIP archive containing Linux arm64 dependencies needs to be uploaded to S3 as a pre-requisite to Create Agent Runtime.

For more information, please refer to the documentation.

// S3 bucket containing the agent core
const codeBucket = new s3.Bucket(this, "AgentCode", {
  bucketName: "my-code-bucket",
  removalPolicy: RemovalPolicy.DESTROY, // For demo purposes
});

// the bucket above needs to contain the agent code

const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromS3(
  {
    bucketName: codeBucket.bucketName,
    objectKey: 'deployment_package.zip',
  }, 
  agentcore.AgentCoreRuntime.PYTHON_3_12, 
  ['opentelemetry-instrument', 'main.py']
);

const runtimeInstance = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
});

Option 4: Use an ECR container image URI

Reference an ECR container image directly by its URI. This is useful when you have a pre-existing ECR image URI from CloudFormation parameters or cross-stack references. No IAM permissions are automatically granted - you must ensure the runtime has ECR pull permissions.

// Direct URI reference
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromImageUri(
  "123456789012.dkr.ecr.us-east-1.amazonaws.com/my-agent:v1.0.0"
);

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
});

You can also use CloudFormation parameters or references:

// Using a CloudFormation parameter
const imageUriParam = new cdk.CfnParameter(this, "ImageUri", {
  type: "String",
  description: "Container image URI for the agent runtime",
});

const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromImageUri(
  imageUriParam.valueAsString
);

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
});

Granting Permissions to Invoke Bedrock Models or Inference Profiles

To grant the runtime permissions to invoke Bedrock models or inference profiles:

// Note: This example uses @aws-cdk/aws-bedrock-alpha which must be installed separately
declare const runtime: agentcore.Runtime;

// Define the Bedrock Foundation Model
const model = bedrock.BedrockFoundationModel.ANTHROPIC_CLAUDE_3_7_SONNET_V1_0;

// Grant the runtime permissions to invoke the model
model.grantInvoke(runtime);

// Create a cross-region inference profile for Claude 3.7 Sonnet
const inferenceProfile = bedrock.CrossRegionInferenceProfile.fromConfig({
  geoRegion: bedrock.CrossRegionInferenceProfileRegion.US,
  model: bedrock.BedrockFoundationModel.ANTHROPIC_CLAUDE_3_7_SONNET_V1_0
});

// Grant the runtime permissions to invoke the inference profile
inferenceProfile.grantInvoke(runtime);

Runtime Versioning

Amazon Bedrock AgentCore automatically manages runtime versioning to ensure safe deployments and rollback capabilities. When you create an agent runtime, AgentCore automatically creates version 1 (V1). Each subsequent update to the runtime configuration (such as updating the container image, modifying network settings, or changing protocol configurations) creates a new immutable version. These versions contain complete, self-contained configurations that can be referenced by endpoints, allowing you to maintain different versions for different environments or gradually roll out updates.

Managing Endpoints and Versions

Amazon Bedrock AgentCore automatically manages runtime versioning to provide safe deployments and rollback capabilities. You can follow the steps below to understand how to use versioning with runtime for controlled deployments across different environments.

Step 1: Initial Deployment

When you first create an agent runtime, AgentCore automatically creates Version 1 of your runtime. At this point, a DEFAULT endpoint is automatically created that points to Version 1. This DEFAULT endpoint serves as the main access point for your runtime.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0"),
});

Step 2: Creating Custom Endpoints

After the initial deployment, you can create additional endpoints for different environments. For example, you might create a "production" endpoint that explicitly points to Version 1. This allows you to maintain stable access points for specific environments while keeping the flexibility to test newer versions elsewhere.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0"),
});

const prodEndpoint = runtime.addEndpoint("production", {
  version: "1",
  description: "Stable production endpoint - pinned to v1"
});

Step 3: Runtime Update Deployment

When you update the runtime configuration (such as updating the container image, modifying network settings, or changing protocol configurations), AgentCore automatically creates a new version (Version 2). Upon this update:

• Version 2 is created automatically with the new configuration
• The DEFAULT endpoint automatically updates to point to Version 2
• Any explicitly pinned endpoints (like the production endpoint) remain on their specified versions

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

const agentRuntimeArtifactNew = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v2.0.0");

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifactNew,
});

Step 4: Testing with Staging Endpoints

Once Version 2 exists, you can create a staging endpoint that points to the new version. This staging endpoint allows you to test the new version in a controlled environment before promoting it to production. This separation ensures that production traffic continues to use the stable version while you validate the new version.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

const agentRuntimeArtifactNew = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v2.0.0");

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifactNew,
});

const stagingEndpoint = runtime.addEndpoint("staging", {
  version: "2",
  description: "Staging environment for testing new version"
});

Step 5: Promoting to Production

After thoroughly testing the new version through the staging endpoint, you can update the production endpoint to point to Version 2. This controlled promotion process ensures that you can validate changes before they affect production traffic.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

const agentRuntimeArtifactNew = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v2.0.0");

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifactNew,
});

const prodEndpoint = runtime.addEndpoint("production", {
  version: "2",  // New version added here
  description: "Stable production endpoint"
});

Creating Standalone Runtime Endpoints

RuntimeEndpoint can also be created as a standalone resource.

Example: Creating an endpoint for an existing runtime

// Reference an existing runtime by its ID
const existingRuntimeId = "abc123-runtime-id"; // The ID of an existing runtime

// Create a standalone endpoint
const endpoint = new agentcore.RuntimeEndpoint(this, "MyEndpoint", {
  endpointName: "production",
  agentRuntimeId: existingRuntimeId,
  agentRuntimeVersion: "1", // Specify which version to use
  description: "Production endpoint for existing runtime"
});

Runtime Authentication Configuration

The AgentCore Runtime supports multiple authentication modes to secure access to your agent endpoints. Authentication is configured during runtime creation using the RuntimeAuthorizerConfiguration class's static factory methods.

IAM Authentication (Default)

IAM authentication is the default mode, when no authorizerConfiguration is set then the underlying service use IAM.

Cognito Authentication

To configure AWS Cognito User Pool authentication:

declare const userPool: cognito.UserPool;
declare const userPoolClient: cognito.UserPoolClient;
declare const anotherUserPoolClient: cognito.UserPoolClient;

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
  authorizerConfiguration: agentcore.RuntimeAuthorizerConfiguration.usingCognito(
    userPool, // User Pool (required)
    [userPoolClient, anotherUserPoolClient], // User Pool Clients
  ),
});

JWT Authentication

To configure custom JWT authentication with your own OpenID Connect (OIDC) provider:

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
  authorizerConfiguration: agentcore.RuntimeAuthorizerConfiguration.usingJWT(
    "https://example.com/.well-known/openid-configuration",  // Discovery URL (required)
    ["client1", "client2"],  // Allowed Client IDs (optional)
    ["audience1"]           // Allowed Audiences (optional)
  ),
});

Note: The discovery URL must end with /.well-known/openid-configuration.

OAuth Authentication

To configure OAuth 2.0 authentication:

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
  authorizerConfiguration: agentcore.RuntimeAuthorizerConfiguration.usingOAuth(
    "https://github.com/.well-known/openid-configuration",  
    "oauth_client_123",  
  ),
});

Using a Custom IAM Role

Instead of using the auto-created execution role, you can provide your own IAM role with specific permissions: The auto-created role includes all necessary baseline permissions for ECR access, CloudWatch logging, and X-Ray tracing. When providing a custom role, ensure these permissions are included.

Runtime Network Configuration

The AgentCore Runtime supports two network modes for deployment:

Public Network Mode (Default)

By default, runtimes are deployed in PUBLIC network mode, which provides internet access suitable for less sensitive or open-use scenarios:

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

// Explicitly using public network (this is the default)
const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
  networkConfiguration: agentcore.RuntimeNetworkConfiguration.usingPublicNetwork(),
});

VPC Network Mode

For enhanced security and network isolation, you can deploy your runtime within a VPC:

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

// Create or use an existing VPC
const vpc = new ec2.Vpc(this, 'MyVpc', {
  maxAzs: 2,
});

// Configure runtime with VPC
const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
  networkConfiguration: agentcore.RuntimeNetworkConfiguration.usingVpc(this, {
    vpc: vpc,
    vpcSubnets: { subnetType: ec2.SubnetType.PRIVATE_WITH_EGRESS },
    // Optionally specify security groups, or one will be created automatically
    // securityGroups: [mySecurityGroup],
  }),
});

Managing Security Groups with VPC Configuration

When using VPC mode, the Runtime implements ec2.IConnectable, allowing you to manage network access using the connections property:

const vpc = new ec2.Vpc(this, 'MyVpc', {
  maxAzs: 2,
});

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

// Create runtime with VPC configuration
const runtime = new agentcore.Runtime(this, "MyAgentRuntime", {
  runtimeName: "myAgent",
  agentRuntimeArtifact: agentRuntimeArtifact,
  networkConfiguration: agentcore.RuntimeNetworkConfiguration.usingVpc(this, {
    vpc: vpc,
    vpcSubnets: { subnetType: ec2.SubnetType.PRIVATE_WITH_EGRESS },
  }),
});

// Now you can manage network access using the connections property
// Allow inbound HTTPS traffic from a specific security group
const webServerSecurityGroup = new ec2.SecurityGroup(this, 'WebServerSG', { vpc });
runtime.connections.allowFrom(webServerSecurityGroup, ec2.Port.tcp(443), 'Allow HTTPS from web servers');

// Allow outbound connections to a database
const databaseSecurityGroup = new ec2.SecurityGroup(this, 'DatabaseSG', { vpc });
runtime.connections.allowTo(databaseSecurityGroup, ec2.Port.tcp(5432), 'Allow PostgreSQL connection');

// Allow outbound HTTPS to anywhere (for external API calls)
runtime.connections.allowToAnyIpv4(ec2.Port.tcp(443), 'Allow HTTPS outbound');

Runtime IAM Permissions

The Runtime construct provides convenient methods for granting IAM permissions to principals that need to invoke the runtime or manage its execution role.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});
const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

// Create a runtime
const runtime = new agentcore.Runtime(this, "MyRuntime", {
  runtimeName: "my_runtime",
  agentRuntimeArtifact: agentRuntimeArtifact,
});

// Create a Lambda function that needs to invoke the runtime
const invokerFunction = new lambda.Function(this, "InvokerFunction", {
  runtime: lambda.Runtime.PYTHON_3_12,
  handler: "index.handler",
  code: lambda.Code.fromInline(`
import boto3
def handler(event, context):
    client = boto3.client('bedrock-agentcore')
    # Invoke the runtime...
  `),
});

// Grant permission to invoke the runtime directly
runtime.grantInvokeRuntime(invokerFunction);

// Grant permission to invoke the runtime on behalf of a user
// (requires X-Amzn-Bedrock-AgentCore-Runtime-User-Id header)
runtime.grantInvokeRuntimeForUser(invokerFunction);

// Grant both invoke permissions (most common use case)
runtime.grantInvoke(invokerFunction);

// Grant specific custom permissions to the runtime's execution role
runtime.grant(['bedrock:InvokeModel'], ['arn:aws:bedrock:*:*:*']);

// Add a policy statement to the runtime's execution role
runtime.addToRolePolicy(new iam.PolicyStatement({
  actions: ['s3:GetObject'],
  resources: ['arn:aws:s3:::my-bucket/*'],
}));

Other configuration

Lifecycle configuration

The LifecycleConfiguration input parameter to CreateAgentRuntime lets you manage the lifecycle of runtime sessions and resources in Amazon Bedrock AgentCore Runtime. This configuration helps optimize resource utilization by automatically cleaning up idle sessions and preventing long-running instances from consuming resources indefinitely.

You can configure:

• idleRuntimeSessionTimeout: Timeout in seconds for idle runtime sessions. When a session remains idle for this duration, it will trigger termination. Termination can last up to 15 seconds due to logging and other process completion. Default: 900 seconds (15 minutes)
• maxLifetime: Maximum lifetime for the instance in seconds. Once reached, instances will initialize termination. Termination can last up to 15 seconds due to logging and other process completion. Default: 28800 seconds (8 hours)

For additional information, please refer to the documentation.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

new agentcore.Runtime(this, 'test-runtime', {
  runtimeName: 'test_runtime',
  agentRuntimeArtifact: agentRuntimeArtifact,
  lifecycleConfiguration: {
    idleRuntimeSessionTimeout: Duration.minutes(10),
    maxLifetime: Duration.hours(4),
  },
});

Request header configuration

Custom headers let you pass contextual information from your application directly to your agent code without cluttering the main request payload. This includes authentication tokens like JWT (JSON Web Tokens, which contain user identity and authorization claims) through the Authorization header, allowing your agent to make decisions based on who is calling it. You can also pass custom metadata like user preferences, session identifiers, or trace context using headers prefixed with X-Amzn-Bedrock-AgentCore-Runtime-Custom-, giving your agent access to up to 20 pieces of runtime context that travel alongside each request. This information can be also used in downstream systems like AgentCore Memory that you can namespace based on those characteristics like user_id or aud in claims like line of business.

For additional information, please refer to the documentation.

const repository = new ecr.Repository(this, "TestRepository", {
  repositoryName: "test-agent-runtime",
});

const agentRuntimeArtifact = agentcore.AgentRuntimeArtifact.fromEcrRepository(repository, "v1.0.0");

new agentcore.Runtime(this, 'test-runtime', {
  runtimeName: 'test_runtime',
  agentRuntimeArtifact: agentRuntimeArtifact,
  requestHeaderConfiguration: {
    allowlistedHeaders: ['X-Amzn-Bedrock-AgentCore-Runtime-Custom-H1'],
  },
});

Browser

The Amazon Bedrock AgentCore Browser provides a secure, cloud-based browser that enables AI agents to interact with websites. It includes security features such as session isolation, built-in observability through live viewing, CloudTrail logging, and session replay capabilities.

Additional information about the browser tool can be found in the official documentation

Browser Network modes

The Browser construct supports the following network modes:

1. Public Network Mode (BrowserNetworkMode.usingPublicNetwork()) - Default

   • Allows internet access for web browsing and external API calls
   • Suitable for scenarios where agents need to interact with publicly available websites
   • Enables full web browsing capabilities
   • VPC mode is not supported with this option

2. VPC (Virtual Private Cloud) (BrowserNetworkMode.usingVpc())

   • Select whether to run the browser in a virtual private cloud (VPC).
   • By configuring VPC connectivity, you enable secure access to private resources such as databases, internal APIs, and services within your VPC.

   While the VPC itself is mandatory, these are optional:

   • Subnets - if not provided, CDK will select appropriate subnets from the VPC
   • Security Groups - if not provided, CDK will create a default security group
   • Specific subnet selection criteria - you can let CDK choose automatically

For more information on VPC connectivity for Amazon Bedrock AgentCore Browser, please refer to the official documentation.

Browser Properties

| Name | Type | Required | Description | |------|------|----------|-------------| | browserCustomName | string | Yes | The name of the browser. Must start with a letter and can be up to 48 characters long. Pattern: [a-zA-Z][a-zA-Z0-9_]{0,47} | | description | string | No | Optional description for the browser. Can have up to 200 characters | | networkConfiguration | BrowserNetworkConfiguration | No | Network configuration for browser. Defaults to PUBLIC network mode | | recordingConfig | RecordingConfig | No | Recording configuration for browser. Defaults to no recording | | executionRole | iam.IRole | No | The IAM role that provides permissions for the browser to access AWS services. A new role will be created if not provided | | tags | { [key: string]: string } | No | Tags to apply to the browser resource | | browserSigning | BrowserSigning | No | Browser signing configuration. Defaults to DISABLED |

Basic Browser Creation

// Create a basic browser with public network access
const browser = new agentcore.BrowserCustom(this, "MyBrowser", {
  browserCustomName: "my_browser",
  description: "A browser for web automation",
});

Browser with Tags

// Create a browser with custom tags
const browser = new agentcore.BrowserCustom(this, "MyBrowser", {
  browserCustomName: "my_browser",
  description: "A browser for web automation with tags",
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingPublicNetwork(),
  tags: {
    Environment: "Production",
    Team: "AI/ML",
    Project: "AgentCore",
  },
});

Browser with VPC

const browser = new agentcore.BrowserCustom(this, 'BrowserVpcWithRecording', {
  browserCustomName: 'browser_recording',
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingVpc(this, {
    vpc: new ec2.Vpc(this, 'VPC', { restrictDefaultSecurityGroup: false }),
  }),
});

Browser exposes a connections property. This property returns a connections object, which simplifies the process of defining and managing ingress and egress rules for security groups in your AWS CDK applications. Instead of directly manipulating security group rules, you interact with the Connections object of a construct, which then translates your connectivity requirements into the appropriate security group rules. For instance:

const vpc = new ec2.Vpc(this, 'testVPC');

const browser = new agentcore.BrowserCustom(this, 'test-browser', {
  browserCustomName: 'test_browser',
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingVpc(this, {
    vpc: vpc,
  }),
});

browser.connections.addSecurityGroup(new ec2.SecurityGroup(this, 'AdditionalGroup', { vpc }));

So security groups can be added after the browser construct creation. You can use methods like allowFrom() and allowTo() to grant ingress access to/egress access from a specified peer over a given portRange. The Connections object automatically adds the necessary ingress or egress rules to the security group(s) associated with the calling construct.

Browser with Recording Configuration

// Create an S3 bucket for recordings
const recordingBucket = new s3.Bucket(this, "RecordingBucket", {
  bucketName: "my-browser-recordings",
  removalPolicy: RemovalPolicy.DESTROY, // For demo purposes
});

// Create browser with recording enabled
const browser = new agentcore.BrowserCustom(this, "MyBrowser", {
  browserCustomName: "my_browser",
  description: "Browser with recording enabled",
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingPublicNetwork(),
  recordingConfig: {
    enabled: true,
    s3Location: {
      bucketName: recordingBucket.bucketName,
      objectKey: "browser-recordings/",
    },
  },
});

Browser with Custom Execution Role

// Create a custom execution role
const executionRole = new iam.Role(this, "BrowserExecutionRole", {
  assumedBy: new iam.ServicePrincipal("bedrock-agentcore.amazonaws.com"),
  managedPolicies: [
    iam.ManagedPolicy.fromAwsManagedPolicyName("AmazonBedrockAgentCoreBrowserExecutionRolePolicy"),
  ],
});

// Create browser with custom execution role
const browser = new agentcore.BrowserCustom(this, "MyBrowser", {
  browserCustomName: "my_browser",
  description: "Browser with custom execution role",
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingPublicNetwork(),
  executionRole: executionRole,
});

Browser with S3 Recording and Permissions

// Create an S3 bucket for recordings
const recordingBucket = new s3.Bucket(this, "RecordingBucket", {
  bucketName: "my-browser-recordings",
  removalPolicy: RemovalPolicy.DESTROY, // For demo purposes
});

// Create browser with recording enabled
const browser = new agentcore.BrowserCustom(this, "MyBrowser", {
  browserCustomName: "my_browser",
  description: "Browser with recording enabled",
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingPublicNetwork(),
  recordingConfig: {
    enabled: true,
    s3Location: {
      bucketName: recordingBucket.bucketName,
      objectKey: "browser-recordings/",
    },
  },
});

// The browser construct automatically grants S3 permissions to the execution role
// when recording is enabled, so no additional IAM configuration is needed

Browser with Browser signing

AI agents need to browse the web on your behalf. When your agent visits a website to gather information, complete a form, or verify data, it encounters the same defenses designed to stop unwanted bots: CAPTCHAs, rate limits, and outright blocks.

Amazon Bedrock AgentCore Browser supports Web Bot Auth. Web Bot Auth is a draft IETF protocol that gives agents verifiable cryptographic identities. When you enable Web Bot Auth in AgentCore Browser, the service issues cryptographic credentials that websites can verify. The agent presents these credentials with every request. The WAF may now additionally check the signature, confirm it matches a trusted directory, and allow the request through if verified bots are allowed by the domain owner and other WAF checks are clear.

To enable the browser to sign requests using the Web Bot Auth protocol, create a browser tool with the browserSigning configuration:

const browser = new agentcore.BrowserCustom(this, 'test-browser', {
  browserCustomName: 'test_browser',
  browserSigning: agentcore.BrowserSigning.ENABLED
});

Browser IAM Permissions

The Browser construct provides convenient methods for granting IAM permissions:

// Create a browser
const browser = new agentcore.BrowserCustom(this, "MyBrowser", {
  browserCustomName: "my_browser",
  description: "Browser for web automation",
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingPublicNetwork(),
});

// Create a role that needs access to the browser
const userRole = new iam.Role(this, "UserRole", {
  assumedBy: new iam.ServicePrincipal("lambda.amazonaws.com"),
});

// Grant read permissions (Get and List actions)
browser.grantRead(userRole);

// Grant use permissions (Start, Update, Stop actions)
browser.grantUse(userRole);

// Grant specific custom permissions
browser.grant(userRole, "bedrock-agentcore:GetBrowserSession");

Code Interpreter

The Amazon Bedrock AgentCore Code Interpreter enables AI agents to write and execute code securely in sandbox environments, enhancing their accuracy and expanding their ability to solve complex end-to-end tasks. This is critical in Agentic AI applications where the agents may execute arbitrary code that can lead to data compromise or security risks. The AgentCore Code Interpreter tool provides secure code execution, which helps you avoid running into these issues.

For more information about code interpreter, please refer to the official documentation

Code Interpreter Network Modes

The Code Interpreter construct supports the following network modes:

1. Public Network Mode (CodeInterpreterNetworkMode.usingPublicNetwork()) - Default

   • Allows internet access for package installation and external API calls
   • Suitable for development and testing environments
   • Enables downloading Python packages from PyPI

2. Sandbox Network Mode (CodeInterpreterNetworkMode.usingSandboxNetwork())

   • Isolated network environment with no internet access
   • Suitable for production environments with strict security requirements
   • Only allows access to pre-installed packages and local resources

3. VPC (Virtual Private Cloud) (CodeInterpreterNetworkMode.usingVpc())

   • Select whether to run the browser in a virtual private cloud (VPC).
   • By configuring VPC connectivity, you enable secure access to private resources such as databases, internal APIs, and services within your VPC.

   While the VPC itself is mandatory, these are optional:

   • Subnets - if not provided, CDK will select appropriate subnets from the VPC
   • Security Groups - if not provided, CDK will create a default security group
   • Specific subnet selection criteria - you can let CDK choose automatically

For more information on VPC connectivity for Amazon Bedrock AgentCore Browser, please refer to the official documentation.

Code Interpreter Properties

| Name | Type | Required | Description | |------|------|----------|-------------| | codeInterpreterCustomName | string | Yes | The name of the code interpreter. Must start with a letter and can be up to 48 characters long. Pattern: [a-zA-Z][a-zA-Z0-9_]{0,47} | | description | string | No | Optional description for the code interpreter. Can have up to 200 characters | | executionRole | iam.IRole | No | The IAM role that provides permissions for the code interpreter to access AWS services. A new role will be created if not provided | | networkConfiguration | CodeInterpreterNetworkConfiguration | No | Network configuration for code interpreter. Defaults to PUBLIC network mode | | tags | { [key: string]: string } | No | Tags to apply to the code interpreter resource |

Basic Code Interpreter Creation

// Create a basic code interpreter with public network access
const codeInterpreter = new agentcore.CodeInterpreterCustom(this, "MyCodeInterpreter", {
  codeInterpreterCustomName: "my_code_interpreter",
  description: "A code interpreter for Python execution",
});

Code Interpreter with VPC

const codeInterpreter = new agentcore.CodeInterpreterCustom(this, "MyCodeInterpreter", {
  codeInterpreterCustomName: "my_sandbox_interpreter",
  description: "Code interpreter with isolated network access",
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingVpc(this, {
    vpc: new ec2.Vpc(this, 'VPC', { restrictDefaultSecurityGroup: false }),
  }),
});

Code Interpreter exposes a connections property. This property returns a connections object, which simplifies the process of defining and managing ingress and egress rules for security groups in your AWS CDK applications. Instead of directly manipulating security group rules, you interact with the Connections object of a construct, which then translates your connectivity requirements into the appropriate security group rules. For instance:

const vpc = new ec2.Vpc(this, 'testVPC');

const codeInterpreter = new agentcore.CodeInterpreterCustom(this, "MyCodeInterpreter", {
  codeInterpreterCustomName: "my_sandbox_interpreter",
  description: "Code interpreter with isolated network access",
  networkConfiguration: agentcore.BrowserNetworkConfiguration.usingVpc(this, {
    vpc: vpc,
  }),
});

codeInterpreter.connections.addSecurityGroup(new ec2.SecurityGroup(this, 'AdditionalGroup', { vpc }));

So security groups can be added after the browser construct creation. You can use methods like allowFrom() and allowTo() to grant ingress access to/egress access from a specified peer over a given portRange. The Connections object automatically adds the necessary ingress or egress rules to the security group(s) associated with the calling construct.

Code Interpreter with Sandbox Network Mode

// Create code interpreter with sandbox network mode (isolated)
const codeInterpreter = new agentcore.CodeInterpreterCustom(this, "MyCodeInterpreter", {
  codeInterpreterCustomName: "my_sandbox_interpreter",
  description: "Code interpreter with isolated network access",
  networkConfiguration: agentcore.CodeInterpreterNetworkConfiguration.usingSandboxNetwork(),
});

Code Interpreter with Custom Execution Role

// Create a custom execution role
const executionRole = new iam.Role(this, "CodeInterpreterExecutionRole", {
  assumedBy: new iam.ServicePrincipal("bedrock-agentcore.amazonaws.com"),
});

// Create code interpreter with custom execution role
const codeInterpreter = new agentcore.CodeInterpreterCustom(this, "MyCodeInterpreter", {
  codeInterpreterCustomName: "my_code_interpreter",
  description: "Code interpreter with custom execution role",
  networkConfiguration: agentcore.CodeInterpreterNetworkConfiguration.usingPublicNetwork(),
  executionRole: executionRole,
});

Code Interpreter IAM Permissions

The Code Interpreter construct provides convenient methods for granting IAM permissions:

// Create a code interpreter
const codeInterpreter = new agentcore.CodeInterpreterCustom(this, "MyCodeInterpreter", {
  codeInterpreterCustomName: "my_code_interpreter",
  description: "Code interpreter for Python execution",
  networkConfiguration: agentcore.CodeInterpreterNetworkConfiguration.usingPublicNetwork(),
});

// Create a role that needs access to the code interpreter
const userRole = new iam.Role(this, "UserRole", {
  assumedBy: new iam.ServicePrincipal("lambda.amazonaws.com"),
});

// Grant read permissions (Get and List actions)
codeInterpreter.grantRead(userRole);

// Grant use permissions (Start, Invoke, Stop actions)
codeInterpreter.grantUse(userRole);

// Grant specific custom permissions
codeInterpreter.grant(userRole, "bedrock-agentcore:GetCodeInterpreterSession");

Code interpreter with tags

// Create code interpreter with sandbox network mode (isolated)
const codeInterpreter = new agentcore.CodeInterpreterCustom(this, "MyCodeInterpreter", {
  codeInterpreterCustomName: "my_sandbox_interpreter",
  description: "Code interpreter with isolated network access",
  networkConfiguration: agentcore.CodeInterpreterNetworkConfiguration.usingPublicNetwork(),
  tags: {
    Environment: "Production",
    Team: "AI/ML",
    Project: "AgentCore",
  },
});

Gateway

The Gateway construct provides a way to create Amazon Bedrock Agent Core Gateways, which serve as integration points between agents and external services.

Gateway Properties

| Name | Type | Required | Description | |------|------|----------|-------------| | gatewayName | string | Yes | The name of the gateway. Valid characters are a-z, A-Z, 0-9, _ (underscore) and - (hyphen). Maximum 100 characters | | description | string | No | Optional description for the gateway. Maximum 200 characters | | protocolConfiguration | IGatewayProtocolConfig | No | The protocol configuration for the gateway. Defaults to MCP protocol | | authorizerConfiguration | IGatewayAuthorizerConfig | No | The authorizer configuration for the gateway. Defaults to Cognito | | exceptionLevel | GatewayExceptionLevel | No | The verbosity of exception messages. Use DEBUG mode to see granular exception messages | | kmsKey | kms.IKey | No | The AWS KMS key used to encrypt data associated with the gateway | | role | iam.IRole | No | The IAM role that provides permissions for the gateway to access AWS services. A new role will be created if not provided | | tags | { [key: string]: string } | No | Tags for the gateway. A list of key:value pairs of tags to apply to this Gateway resource |

Basic Gateway Creation

The protocol configuration defaults to MCP and the inbound auth configuration uses Cognito (it is automatically created on your behalf).

// Create a basic gateway with default MCP protocol and Cognito authorizer
const gateway = new agentcore.Gateway(this, "MyGateway", {
  gatewayName: "my-gateway",
});

Protocol configuration

Currently MCP is the only protocol available. To configure it, use the protocol property with McpProtocolConfiguration:

• Instructions: Guidance for how to use the gateway with your tools
• Semantic search: Smart tool discovery that finds the right tools without typical limits. It improves accuracy by finding relevant tools based on context
• Supported versions: Which MCP protocol versions the gateway can use

const gateway = new agentcore.Gateway(this, "MyGateway", {
  gatewayName: "my-gateway",
  protocolConfiguration: new agentcore.McpProtocolConfiguration({
    instructions: "Use this gateway to connect to external MCP tools",
    searchType: agentcore.McpGatewaySearchType.SEMANTIC,
    supportedVersions: [agentcore.MCPProtocolVersion.MCP_2025_03_26],
  }),
});

Inbound authorization

Before you create your gateway, you must set up inbound authorization. Inbound authorization validates users who attempt to access targets through your AgentCore gateway. By default, if not provided, the construct will create and configure Cognito as the default identity provider (inbound Auth setup). AgentCore supports the following types of inbound authorization:

JSON Web Token (JWT) – A secure and compact token used for authorization. After creating the JWT, you specify it as the authorization configuration when you create the gateway. You can create a JWT with any of the identity providers at Provider setup and configuration.

You can configure a custom authorization provider using the inboundAuthorizer property with GatewayAuthorizer.usingCustomJwt(). You need to specify an OAuth discovery server and client IDs/audiences when you create the gateway. You can specify the following:

• Discovery Url — String that must match the pattern ^.+/.well-known/openid-configuration$ for OpenID Connect discovery URLs
• At least one of the below options depending on the chosen identity provider.
• Allowed audiences — List of allowed audiences for JWT tokens
• Allowed clients — List of allowed client identifiers

const gateway = new agentcore.Gateway(this, "MyGateway", {
  gatewayName: "my-gateway",
  authorizerConfiguration: agentcore.GatewayAuthorizer.usingCustomJwt({
    discoveryUrl: "https://auth.example.com/.well-known/openid-configuration",
    allowedAudience: ["my-app"],
    allowedClients: ["my-client-id"],
  }),
});

IAM – Authorizes through the credentials of the AWS IAM identity trying to access the gateway.

const gateway = new agentcore.Gateway(this, "MyGateway", {
  gatewayName: "my-gateway",
  authorizerConfiguration: agentcore.GatewayAuthorizer.usingAwsIam(),
});

// Grant access to a Lambda function's role
const lambdaRole = new iam.Role(this, "LambdaRole", {
  assumedBy: new iam.ServicePrincipal("lambda.amazonaws.com"),
});

// The Lambda needs permission to invoke the gateway
gateway.grantInvoke(lambdaRole);

Gateway with KMS Encryption

You can provide a KMS key, and configure the authorizer as well as the protocol configuration.

// Create a KMS key for encryption
const encryptionKey = new kms.Key(this, "GatewayEncryptionKey", {
  enableKeyRotation: true,
  description: "KMS key for gateway encryption",
});

// Create gateway with KMS encryption
const gateway = new agentcore.Gateway(this, "MyGateway", {
  gatewayName: "my-encrypted-gateway",
  description: "Gateway with KMS encryption",
  protocolConfiguration: new agentcore.McpProtocolConfiguration({
    instructions: "Use this gateway to connect to external MCP tools",
    searchType: agentcore.McpGatewaySearchType.SEMANTIC,
    supportedVersions: [agentcore.MCPProtocolVersion.MCP_2025_03_26],
  }),
  authorizerConfiguration: agentcore.GatewayAuthorizer.usingCustomJwt({
    discoveryUrl: "https://auth.example.com/.well-known/openid-configuration",
    allowedAudience: ["my-app"],
    allowedClients: ["my-client-id"],
  }),
  kmsKey: encryptionKey,
  exceptionLevel: agentcore.GatewayExceptionLevel.DEBUG,
});

Gateway with Custom Execution Role

// Create a custom execution role
const executionRole = new iam.Role(this, "GatewayExecutionRole", {
  assumedBy: new iam.ServicePrincipal("bedrock-agentcore.amazonaws.com"),
  managedPolicies: [
    iam.ManagedPolicy.fromAwsManagedPolicyName("AmazonBedrockAgentCoreGatewayExecutionRolePolicy"),
  ],
});

// Create gateway with custom execution role
const gateway = new agentcore.Gateway(this, "MyGateway", {
  gatewayName: "my-gateway",
  description: "Gateway with custom execution role",
  protocolConfiguration: new agentcore.McpProtocolConfiguration({
    instructions: "Use this gateway to connect to external MCP tools",
    searchType: agentcore.McpGatewaySearchType.SEMANTIC,
    supportedVersions: [agentcore.MCPProtocolVersion.MCP_2025_03_26],
  }),
  authorizerConfiguration: agentcore.GatewayAuthorizer.usingCustomJwt({
    discoveryUrl: "https://auth.example.com/.well-known/openid-configuration",
    allowedAudience: ["my-app"],
    allowedClients: ["my-client-id"],
  }),
  role: executionRole,
});

Gateway IAM Permissions

The Gateway construct provides convenient methods for granting IAM permissions:

// Create a gateway
const gateway = new agentcore.Gateway(this, "MyGateway", {
  gatewayName: "my-gateway",
  description: "Gateway for external service integration",
  protocolConfiguration: new agentcore.McpProtocolConfiguration({
    instructions: "Use this gateway to connect to external MCP tools",
    searchType: agentcore.McpGatewaySearchType.SEMANTIC,
    supportedVersions: [agentcore.MCPProtocolVersion.MCP_2025_03_26],
  }),
  authorizerConfiguration: agentcore.GatewayAuthorizer.usingCustomJwt({
    discoveryUrl: "https://auth.example.com/.well-known/openid-configuration",
    allowedAudience: ["my-app"],
    allowedClients: ["my-client-id"],
  }),
});

// Create a role that needs access to the gateway
const userRole = new iam.Role(this, "UserRole", {
  assumedBy: new iam.ServicePrincipal("lambda.amazonaws.com"),
});

// Grant read permissions (Get and List actions)
gateway.grantRead(userRole);

// Grant manage permissions (Create, Update, Delete actions)
gateway.grantManage(userRole);

// Grant specific custom permissions
gateway.grant(userRole, "bedrock-agentcore:GetGateway");

Gateway Target

After Creating gateways, you can add targets which define the tools that your gateway will host. Gateway supports multiple target types including Lambda functions and API specifications (either OpenAPI schemas or Smithy models). Gateway allows you to attach multiple targets to a Gateway and you can change the targets / tools attached to a gateway at any point. Each target can have its own credential provider attached enabling you to securely access targets whether they need IAM, API Key, or OAuth credentials.

Gateway Target Properties

| Name | Type | Required | Description | |------|------|----------|-------------| | gatewayTargetName | string | Yes | The name of the gateway target. Valid characters are a-z, A-Z, 0-9, _ (underscore) and - (hyphen) | | description | string | No | Optional description for the gateway target. Maximum 200 characters | | gateway | IGateway | Yes | The gateway this target belongs to | | targetConfiguration | ITargetConfiguration | Yes | The target configuration (Lambda, OpenAPI, or Smithy). Note: Users typically don't create this directly. When using convenience methods like GatewayTarget.forLambda(), GatewayTarget.forOpenApi(), GatewayTarget.forSmithy() or the gateway's addLambdaTarget(), addOpenApiTarget(), addSmithyTarget() methods, this configuration is created internally for you. Only needed when using the GatewayTarget constructor directly for advanced scenarios. | | credentialProviderConfigurations | IGatewayCredentialProvider[] | No | Credential providers for authentication. Defaults to [GatewayCredentialProvider.fromIamRole()]. Use GatewayCredentialProvider.fromApiKeyIdentityArn(), GatewayCredentialProvider.fromOauthIdentityArn(), or GatewayCredentialProvider.fromIamRole() | | validateOpenApiSchema | boolean | No | (OpenAPI targets only) Whether to validate the OpenAPI schema at synthesis time. Defaults to true. Only applies to inline and local asset schemas. For more information refer here https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-schema-openapi.html |

This approach gives you full control over the configuration but is typically not necessary for most use cases.

Targets types

You can create the following targets types:

Lambda Target: Lambda targets allow you to connect your gateway to AWS Lambda functions that implement your tools. This is useful when you want to execute custom code in response to tool invocations.

• Supports GATEWAY_IAM_ROLE credential provider only
• Ideal for custom serverless function integration
• Need tool schema (tool schema is a blueprint that describes the functions your Lambda provides to AI agents). The construct provide 3 ways to upload a tool schema to Lambda target
• When using the default IAM authentication (no credentialProviderConfigurations specified), the construct automatocally grants the gateway role permission to invoke your Lambda function (lambda:InvokeFunction).

OpenAPI Schema Target : OpenAPI widely used standard for describing RESTful APIs. Gateway supports OpenAPI 3.0 specifications for defining API targets. It connects to REST APIs using OpenAPI specifications

• Supports OAUTH and API_KEY credential providers (Do not support IAM, you must provide credentialProviderConfigurations)
• Ideal for integrating with external REST services
• Need API schema. The construct provide 3 ways to upload a API schema to OpenAPI target

Smithy Model Target : Smithy is a language for defining services and software development kits (SDKs). Smithy models provide a more structured approach to defining APIs compared to OpenAPI, and are particularly useful for connecting to AWS services. AgentCore Gateway supports built-in AWS service models only. It connects to services using Smithy model definitions

• Supports OAUTH and API_KEY credential providers
• Ideal for AWS service integrations
• Need API schema. The construct provide 3 ways to upload a API schema to Smity target
• When using the default IAM authentication (no credentialProviderConfigurations specified), The construct only grants permission to read the Smithy schema file from S3. You MUST manually grant permissions for the gateway role to invoke the actual Smithy API endpoints

Note: For Smithy model targets that access AWS services, your Gateway's execution role needs permissions to access those services. For example, for a DynamoDB target, your execution role needs permissions to perform DynamoDB operations. This is not managed by the construct due to the large number of options. Please refer to Smithy Model Permission for example.

MCP Server Target: Model Context Protocol (MCP) servers provide external tools, data access, and custom functions for AI agents. MCP servers enable agents to interact with external systems and services through a standardized protocol. Gateway automatically discovers and indexes available tools from MCP servers through synchronization.

Key Features:

• Requires explicit authentication configuration (OAuth2 recommended, empty array for NoAuth)
• Ideal for connecting to external MCP-compliant servers
• The endpoint must use HTTPS protocol
• Supported MCP protocol versions: 2025-06-18, 2025-03-26
• Automatic tool discovery through synchronization

Synchronization Behavior:

MCP Server targets require synchronization to discover and index available tools:

• Implicit Synchronization (Automatic): Tool discovery happens automatically during:

  • Target creation (CreateGatewayTarget)
  • Target updates (UpdateGatewayTarget)
  • The Gateway calls the MCP server's tools/list endpoint and indexes tools without user intervention

• Explicit Synchronization (Manual): When the MCP server's tools change independently (new tools added, schemas modified, tools removed):

  • The Gateway's tool catalog becomes stale
  • Call the SynchronizeGatewayTargets API to refresh the catalog
  • Use the grantSync() method to grant permissions to Lambda functions, CI/CD pipelines, or scheduled tasks that will trigger synchronization

**Authentication & P