This content originally appeared on DEV Community and was authored by Milad Rezaeighale
We’re living in the era of agents and agentic workflows. Frameworks like LangChain, LlamaIndex, CrewAI, and others make it easier than ever to design complex single- or multi-agent systems that can plan, reason, and act. It’s exciting to see these frameworks powering demos that wow technical teams and spark imagination.
But here’s the catch: no matter how clever the prompt chaining is, or how impressive the reasoning looks on screen, it doesn’t create real business value until it’s deployed into production and embedded into the company’s workflows. For executives, a polished demo is nice — but a production-ready agent that’s delivering measurable outcomes is what really matters.
This is where [Amazon Bedrock AgentCore (https://aws.amazon.com/bedrock/agentcore/) comes in. It enables you to deploy and operate highly effective agents securely, at scale, using any framework or model — including open-source options like LangChain or LlamaIndex. With AgentCore, you can accelerate AI agents into production with the scale, reliability, and security essential for real-world use. It offers tools to enhance agent capabilities, purpose-built infrastructure to scale securely, and controls to ensure trustworthiness. Best of all, its services are composable and framework-agnostic, so you don’t have to choose between open-source flexibility and enterprise-grade robustness.
From Theory to Practice
We’ve talked about why production deployment matters and how Amazon Bedrock AgentCore is designed to make it easier, faster, and more secure. Now, without any further explanation, let’s get straight to the point — in the rest of this article, I’ll show you exactly how you can deploy your own agent into production with AgentCore.
We’ve talked about why production deployment matters and how Amazon Bedrock AgentCore is designed to make it easier, faster, and more secure. Now, without any further explanation, let’s get straight to the point — in this article, we’ll keep things simple by using the AgentCore Starter Toolkit, which gives you the perfect opportunity for quick prototyping and testing. In the following steps, I’ll walk you through how to use it to deploy your own agent into production with AgentCore.
Before starting, ensure your AWS CLI is configured and authenticated. You can either:
- Use AWS SSO via aws configure sso, or
- Use access keys via aws configure
This configuration must be done in the same environment where you will run your Python script so that boto3 can authenticate and invoke your Bedrock AgentCore runtime successfully.
Step 1 – Configuration
First, install the Bedrock AgentCore Starter Toolkit. This toolkit gives you a ready-made environment to quickly prototype and test agents before taking them to production.
pip install bedrock-agentcore-starter-toolkit
Once installed, you’ll have access to CLI commands and project templates that speed up setup so you can focus on building and deploying your agent.
Step 2 – Create Your Project Folder
Next, set up a simple project structure for your agent. This will keep your code, dependencies, and package definition organized for deployment.
Project Folder Structure
your_project_directory/
├── my_agent.py
├── requirements.txt
└── __init__.py
File Contents
my_agent.py
from strands import Agent
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands.models import BedrockModel
import json
model_id = "eu.anthropic.claude-3-7-sonnet-20250219-v1:0"
model = BedrockModel(
model_id=model_id,
)
agent = Agent(
model=model
)
app = BedrockAgentCoreApp()
@app.entrypoint
def invoke(payload):
"""
Invoke the agent with a payload
"""
user_input = payload.get("prompt")
print("User input:", user_input)
response = agent(user_input)
return response.message['content'][0]['text']
if __name__ == "__main__":
app.run()
requirements.txt
strands-agents
bedrock-agentcore
This minimal setup defines:
- my_agent.py — where your agent’s logic lives and integrates with AgentCore.
- requirements.txt — listing dependencies so they can be installed in the runtime environment.
- init.py — ensures the folder is treated as a Python package.
Step 3 – Configure Your Agent
Before deploying, you need to tell the Starter Toolkit which IAM role your agent should use when running in production. This role must have the necessary AgentCore Runtime permissions (see Permissions for AgentCore Runtime).
Run the following command, replacing _*YOUR_IAM_ROLE_ARN *_with the ARN of your IAM role:
Step 4 – Deploy Your Agent
Now that your agent is configured, it’s time to deploy it into production using AgentCore.
Deployment Steps
Step 1 – Configure Your Agent for Deployment
Run the following command, replacing with your IAM role ARN:
agentcore configure --entrypoint my_agent.py -er <YOUR_IAM_ROLE_ARN>
This command will:
Generate a Dockerfile **_and _.dockerignore** for containerizing your agent
Create a .bedrock_agentcore.yaml configuration file with your agent’s runtime settings
While configuring your agent, you’ll be prompted to provide the URI of the Amazon ECR repository where the Docker image will be uploaded. You can either create this repository yourself in the AWS Console and enter its URI, or simply press Enter to have AgentCore create one for you automatically.
You will also be prompted to confirm your dependencies — press Enter to let AgentCore use requirements.txt. For authorization, you can choose the default no to keep IAM.
After completing the prompts, you’ll see a configuration summary showing your agent name, AWS region, account ID, execution role, ECR repository, and authorization method. The configuration is then saved locally in a .bedrock_agentcore.yaml file for use during deployment.
Now you’re ready to launch your agent in production.
Step 5 – Launch Your Agent
With your configuration complete, you can now deploy your agent to AWS with a single command:
agentcore launch
This command will:
- Build a Docker image containing your agent code
- Push the image to Amazon ECR
- Create a Bedrock AgentCore runtime in your AWS account
- Deploy your agent to the cloud so it’s ready for production use
Once complete, you’ll have a production-ready agent running on Amazon Bedrock AgentCore, fully integrated with your chosen framework and secured by AWS IAM.
Step 6 – Invoke the Agent
To test our deployed agent, we’ll create a new file named test.py in the same folder as our project and run the invocation from there.
This script sends a natural-language prompt to the agent and processes the streamed response.
import boto3
import json
# Initialize the Bedrock AgentCore client in the same region as your agent
agentcore_client = boto3.client('bedrock-agentcore', region_name='eu-central-1')
# Your Agent Runtime ARN (from the deployment step)
# You can find this in the Bedrock console under your agent’s runtime details,
# or in the deployment confirmation message.
AGENT_RUNTIME = "AOUTR_AGENT_RUNTIME_ARN"
# Prompt to send to the agent
PROMPT = "Please explain how can I become a professional football player?"
# Invoke the agent
boto3_response = agentcore_client.invoke_agent_runtime(
agentRuntimeArn=AGENT_RUNTIME,
qualifier="DEFAULT",
payload=json.dumps({"prompt": PROMPT})
)
# The response is streamed in chunks; read them all into memory
response_body = boto3_response['response']
all_chunks = [chunk for chunk in response_body]
# Combine chunks into one string
complete_response = b''.join(all_chunks).decode('utf-8')
# Attempt to parse JSON output
try:
response_json = json.loads(complete_response)
print(response_json)
except json.JSONDecodeError:
print("Raw response:")
print(complete_response)
How it works:
- boto3.client('bedrock-agentcore') – Creates a client to communicate with the AgentCore Runtime service.
- invoke_agent_runtime() – Sends the prompt to the agent and streams back the response.
- StreamingBody reading – The output is returned in small chunks, which we merge before decoding.
- JSON parsing – If the response is in JSON format, we parse it; otherwise, we display the raw text.
Save the file as test.py in your project folder, then run it from your terminal:
python test.py
You should see the agent’s JSON response (or raw output) printed in the terminal.
This approach ensures you receive the complete, assembled agent output, whether it’s plain text or structured JSON.
Wrapping Up
Amazon Bedrock AgentCore bridges the gap between impressive agent demos and real-world business impact. By following the steps in this guide, you can go from idea to production-ready agent quickly, while leveraging AWS’s scalability, reliability, and security. The sooner your agent moves into production, the sooner it can start delivering measurable value to your business.
Whether you’re experimenting with a single-agent workflow or orchestrating multi-agent systems, AgentCore gives you the tools to operationalize your ideas with confidence. Now it’s your turn—deploy your agent, test it, and see how it performs in the real world.
This content originally appeared on DEV Community and was authored by Milad Rezaeighale
Milad Rezaeighale | Sciencx (2025-08-11T11:05:02+00:00) From Demos to Business Value: Taking Agents to Production with Amazon Bedrock AgentCore. Retrieved from https://www.scien.cx/2025/08/11/from-demos-to-business-value-taking-agents-to-production-with-amazon-bedrock-agentcore/
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