Leveraging Amazon Bedrock AgentCore for Streamlined Agent Deployment

Introduction to AgentCore and Direct Code Deployment

Understanding AgentCore Runtime and Its Benefits

A Comparison of Deployment Methods

Container-Based Deployment Steps

Direct Code Deployment Steps

Step-by-Step Guide to Using Direct Code Deployment

Prerequisites for Direct Code Deployment

Initializing Your Project

Adding Dependencies

Creating Your Agent Implementation

Deploying to AgentCore Runtime

Evaluating When to Choose Direct Code Deployment vs. Container-Based Deployment

Conclusion: Accelerating Development with Direct Code Deployment

About the Authors

Harnessing the Power of Amazon Bedrock AgentCore for Simplified AI Agent Deployment

In the world of AI and machine learning, scalability and ease of deployment are crucial. Amazon’s newest offering, Amazon Bedrock AgentCore, addresses these needs by providing a robust platform for building, deploying, and operating AI agents securely at scale. Let’s dive into what makes this platform essential for developers and how the AgentCore Runtime facilitates a more straightforward deployment process.

What is Amazon Bedrock AgentCore?

Amazon Bedrock AgentCore is an agentic platform designed to streamline the creation and management of agents capable of handling complex tasks efficiently. The AgentCore Runtime is a fully managed service that offers low-latency, serverless environments for deploying agents and associated tools.

Key Features:

1. Session Isolation: Ensures that interactions are securely contained, providing a safe environment for each agent.
2. Support for Multiple Frameworks: Works seamlessly with various popular open-source frameworks.
3. Multimodal Workloads: Can handle multiple types of data input, making agents more versatile.
4. Long-Running Agents: Supports agents that require extended processing times without interruptions.

Simplifying Deployment with Direct Code Upload

Traditionally, deploying AI agents involved intricate Docker setups, requiring a deep understanding of containerization. Developers often found themselves mired in Docker and ECR management, steering focus away from code development. Amazon Bedrock AgentCore offers a simpler path through Direct Code Deployment.

Why Choose Direct Code Deployment?

With this method, developers can easily package their code into a zip archive alongside its dependencies, upload it to Amazon S3, and configure the setup without worrying about Docker complexities. This approach benefits rapid prototyping, lets you iterate faster, and enhances developer productivity.

Here’s a comparative look at Container-Based vs. Direct Code Deployment:

Container-Based Deployment Steps

1. Create a Dockerfile.
2. Build ARM-compatible container.
3. Create and manage ECR repositories.
4. Upload to ECR.
5. Deploy to AgentCore Runtime.

Direct Code Deployment Steps

1. Package code and dependencies into a zip archive.
2. Upload it to S3.
3. Configure the bucket in agent settings.
4. Deploy to AgentCore Runtime.

How to Use Direct Code Deployment

Let’s illustrate the direct code deployment process using an agent created with the Strands Agents SDK. To ensure a smooth setup, here’s what you need beforehand:

Prerequisites:

• Python (versions 3.10 to 3.13).
• A package manager (like uv).
• An AWS account for deployment.
• Access to the Amazon Bedrock model (e.g., Anthropic Claude Sonnet 4.0).

Step-by-Step Guide:

Step 1: Initialize Your Project

Set up a new Python project with the uv package manager, navigating to your project directory afterward.

Step 2: Add Dependencies

Install the necessary libraries using a .toml file or requirements.txt, as shown below:

uv add bedrock-agentcore strands-agents strands-agents-tools
uv add --dev bedrock-agentcore-starter-toolkit
source .venv/bin/activate

Step 3: Create Your agent.py File

Define your AI agent’s behavior in this file. Here’s a sample implementation:

from bedrock_agentcore import BedrockAgentCoreApp 
from strands import Agent, tool 
from strands_tools import calculator  
from strands.models import BedrockModel 
import logging 

app = BedrockAgentCoreApp(debug=True) 

# Logging setup 
logging.basicConfig(level=logging.INFO) 
logger = logging.getLogger(__name__) 

@tool 
def weather(): 
    """ Get weather """  
    return "sunny" 

model_id = "us.anthropic.claude-sonnet-4-20250514-v1:0" 
model = BedrockModel(model_id=model_id)

agent = Agent(model=model, tools=[calculator, weather], system_prompt="You're a helpful assistant.")

@app.entrypoint 
def invoke(payload): 
    """Your AI agent function""" 
    user_input = payload.get("prompt", "Hello!") 
    logger.info("User input: %s", user_input) 
    response = agent(user_input) 
    logger.info("Agent result: %s", response.message) 
    return response.message['content'][0]['text'] 

if __name__ == "__main__": 
    app.run()

Step 4: Deploy to AgentCore Runtime

Configure the deployment using:

agentcore configure --entrypoint agent.py --name

Choose the Code Zip option when prompted for deployment type, and your agent will be deployed seamlessly.

When to Choose Direct Code Over Container-Based Deployment

Comparison Overview:

• Deployment Process: Direct code is simpler, requiring no Docker or ECR management.
• Deployment Time: Updates with direct code are quicker (approx. 10 seconds for updates vs. 30 seconds for containers).
• Artifact Size: Direct code deployment has a size limit of 250MB, while container deployments can reach up to 2GB.
• Customization: Direct code allows for simplified dependency handling, whereas containers can utilize full Docker control.

Final Thoughts

Amazon Bedrock AgentCore’s direct code deployment greatly accelerates development cycles while upholding robust security and scaling properties. This allows developers to concentrate on what truly matters—building and refining their AI agents.

Interested in getting started? Check out the AWS documentation for further insights!

About the Authors

• Chaitra Mathur – GenAI Specialist Solutions Architect at AWS, focusing on scalable generative AI platforms.
• Qingwei Li – Machine Learning Specialist at Amazon Web Services, dedicated to building AI solutions across various sectors.
• Kosti Vasilakakis – Principal PM at AWS on the Agentic AI team, involved in developing foundational AI services like Bedrock AgentCore.

Dive into the world of cloud-based AI development with ease and efficiency, leveraging the innovative capabilities of Amazon Bedrock AgentCore!