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Basic AgentCore Runtime - Terraform

This pattern demonstrates the simplest deployment of an AgentCore Runtime using Terraform. It creates a basic agent without additional tools like Memory, Code Interpreter, or Browser.

Table of Contents

Overview

This Terraform configuration creates a minimal AgentCore deployment that includes:

  • AgentCore Runtime: Hosts a simple Strands agent
  • ECR Repository: Stores the Docker container image
  • IAM Roles: Provides necessary permissions
  • CodeBuild Project: Automatically builds the ARM64 Docker image

This makes it ideal for:

  • Learning AgentCore basics with Terraform
  • Quick prototyping and experimentation
  • Understanding the core deployment pattern
  • Building a foundation before adding complexity

Architecture

Architecture Diagram

What's Included

This Terraform configuration creates:

  • S3 Bucket: Stores agent source code for version-controlled builds
  • ECR Repository: Container registry for the agent Docker image
  • CodeBuild Project: Automated Docker image building and pushing
  • IAM Roles: Execution roles for the agent and CodeBuild
  • AgentCore Runtime: Serverless agent runtime with the deployed container

Agent Code Management

The agent-code/ directory contains your agent's source files:

  • basic_agent.py - Agent implementation
  • Dockerfile - Container configuration
  • requirements.txt - Python dependencies

Automatic Change Detection:

  • Terraform archives the agent-code/ directory
  • Uploads to S3 with MD5-based versioning
  • CodeBuild pulls from S3 and builds the Docker image
  • Any changes to files trigger automatic rebuild (new files, modifications, deletions)

Prerequisites

Required Tools

  1. Terraform (>= 1.6)

    Note: brew install terraform provides v1.5.7 (deprecated). Use tfenv or direct download for >= 1.6.

  2. AWS CLI (configured with credentials)

    aws configure

  3. Python 3.11+ (for testing scripts)

    python --version  # Verify Python 3.11 or later
pip install boto3

  4. Docker (for local testing, optional)

AWS Account Requirements

  • AWS Account with appropriate permissions
  • Access to Amazon Bedrock models

Quick Start

1. Configure Variables

Copy the example variables file and customize:

cp terraform.tfvars.example terraform.tfvars

Edit terraform.tfvars with your preferred values.

2. Initialize Terraform

See State Management Options in the main README for detailed guidance on local vs. remote state.

Quick start with local state:

terraform init

For team collaboration, use remote state - see the main README for setup instructions.

3. Review the Plan

terraform plan

4. Deploy

Method 1: Using Deploy Script (Recommended)

Make the script executable (first-time only):

chmod +x deploy.sh

Then deploy:

./deploy.sh

The deploy script:

  • Validates Terraform configuration
  • Shows deployment plan
  • Prompts for confirmation
  • Applies changes

Method 2: Direct Terraform Commands

terraform apply

When prompted, type yes to confirm the deployment.

Note: The deployment process includes:

  1. Creating ECR repository
  2. Building Docker image via CodeBuild
  3. Creating AgentCore Runtime

Total deployment time: ~3-5 minutes

5. Get Outputs

After deployment completes:

terraform output

Example output:

agent_runtime_id = "AGENT1234567890"
agent_runtime_arn = "arn:aws:bedrock-agentcore:<us-west-2>:123456789012:agent-runtime/AGENT1234567890"
ecr_repository_url = "123456789012.dkr.ecr.us-west-2.amazonaws.com/agentcore-basic-basic-agent"

Testing the Agent

Prerequisites for Testing

Before testing, ensure you have the required packages installed:

Option A: Using uv (Recommended)

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install boto3  # Required for agent invocation

Option B: System-wide installation

pip install boto3  # Required for agent invocation

Note: boto3 is required for the test script to invoke the agent runtime via AWS API.

Option 1: Using Test Script (Recommended)

# Run the test suite
python test_basic_agent.py $(terraform output -raw agent_runtime_arn)

Option 2: Using AWS CLI

# Get the runtime ARN from outputs
RUNTIME_ARN=$(terraform output -raw agent_runtime_arn)

# Invoke the agent
aws bedrock-agentcore invoke-agent-runtime \
  --agent-runtime-arn $RUNTIME_ARN \
  --qualifier DEFAULT \
  --payload $(echo '{"prompt": "Hello, introduce yourself"}' | base64) \
  response.json

# View the response
cat response.json | jq -r '.response'

Option 3: Using AWS Console

  1. Navigate to Amazon Bedrock console
  2. Go to AgentCore → Runtimes
  3. Select your runtime
  4. Use the "Test" feature to send queries

Sample Queries

Try these queries to test your basic agent:

  1. Simple Math:

    {"prompt": "What is 2+2?"}

  2. General Knowledge:

    {"prompt": "What is the capital of France?"}

  3. Explanation Request:

    {"prompt": "Explain what Amazon Bedrock is in simple terms"}

  4. Creative Task:

    {"prompt": "Write a haiku about cloud computing"}

Customization

Modify Agent Code

Edit files in agent-code/ and deploy:

  • basic_agent.py - Agent logic and system prompt
  • Dockerfile - Container configuration
  • requirements.txt - Python dependencies

Changes are automatically detected and trigger rebuild. Run terraform apply to deploy.

Environment Variables

Add to terraform.tfvars:

environment_variables = {
  LOG_LEVEL = "DEBUG"
}

Network Mode

Set network_mode = "PRIVATE" for VPC deployment (requires additional VPC configuration).

File Structure

basic-runtime/
├── main.tf                      # AgentCore runtime resource
├── variables.tf                 # Input variables
├── outputs.tf                   # Output values
├── versions.tf                  # Provider configuration
├── iam.tf                       # IAM roles and policies
├── s3.tf                        # S3 bucket for source code
├── ecr.tf                       # ECR repository
├── codebuild.tf                 # Docker build automation
├── buildspec.yml                # CodeBuild build specification
├── terraform.tfvars.example     # Example configuration
├── backend.tf.example           # Remote state example
├── test_basic_agent.py          # Automated test script
├── agent-code/                  # Agent source code
│   ├── basic_agent.py          # Agent implementation
│   ├── Dockerfile              # Container configuration
│   └── requirements.txt        # Python dependencies
├── scripts/                     # Build automation scripts
│   └── build-image.sh          # CodeBuild trigger & verification
├── deploy.sh                    # Deployment helper script
├── destroy.sh                   # Cleanup helper script
├── .gitignore                   # Git ignore patterns
└── README.md                    # This file

Troubleshooting

CodeBuild Fails

If the Docker build fails:

  1. Check CodeBuild logs:

    aws codebuild batch-get-builds \
  --ids $(terraform output -raw codebuild_project_name) \
  --region us-west-2

  2. Common issues:

    • Network connectivity issues
    • ECR authentication problems
    • Python dependency conflicts

Runtime Creation Fails

If the runtime creation fails:

  1. Verify the Docker image exists:

    aws ecr describe-images \
  --repository-name $(terraform output -raw ecr_repository_url | cut -d'/' -f2) \
  --region us-west-2

  2. Check IAM role permissions

  3. Verify Bedrock AgentCore service quotas

Agent Invocation Fails

If invoking the agent fails:

  1. Check runtime status in AWS Console
  2. Review CloudWatch Logs for the runtime
  3. Verify Bedrock model access permissions

Cleanup

Destroy All Resources

Make the script executable (first-time only):

chmod +x destroy.sh

Then cleanup:

./destroy.sh

Or use Terraform directly:

terraform destroy

Verify Cleanup

Confirm all resources are deleted:

# Check ECR repositories
aws ecr describe-repositories --region us-west-2 | grep agentcore-basic

# Check AgentCore runtimes
aws bedrock-agentcore list-agent-runtimes --region us-west-2

Pricing

For current pricing information, please refer to:

Note: Actual costs depend on your usage patterns, AWS region, and specific services consumed.

Next Steps

Explore Other Patterns

Resources

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.