How to be deployed to AI Agent with Amazon Badric Agent Corps

The Amazon Badric Agent Core is a systematic service that makes AI agents safe to safely build, deploy and operate on AWS. It works with framework such as Strands Agents, Lang Graphs, Croovie, and Lemandex, taking care of complex tasks such as the Run Time Management, IAM character, and observation.

In this guide, you will set your environment, create a simple AI agent locally and test them, deploy it with an agentcore star toolkit, and ask for it via AWS SDK.

The table of content

Provisions

Before starting, make sure you have:

• An AWS account has been created with credentials.

• AWS Cli is installing and working.

• Izgar 3.10 or later installed.

• Boto3 was installed.

• Access to the model in the Amazon Bedrock Console (for example, the Entropic Claude Swant 4.0).

Step 1: AWS CLI

First, if you do not already have, install AWS Cli. On Linux or Macos: AWS CLI Setup Guide.

Next, Combination A profile with AWS SSO:

aws configure sso --profile my-profile

You will be indicated to enter the details such as:

• Start SSO URL – URL for your AWS Organization’s IAM ID Center Portal.

• SSO Region – AWS region where the IAM identification center is formed.

• Account ID – The AWS account you want to access.

• The character’s name – IAM character you want to assume in this account.

• The default region – The region will be used when making applications.

• The default output format – for example, jsonFor, for, for,. yamlOr table.

It produces a new profile called my-profile In your AWS CLI setting, allows you to use this identity to communicate with AWS services.

Next, you have to confirm your identity. Once your profile is formed, confirm that CLI is correctly verifying with AWS walking:

aws sts get-caller-identity --profile my-profile

This command returns details about your identity, including:

• Account – AWS Account ID against which you are certified.

• User – Your IAM character or a unique identifier of the user.

• Cooker – The full name of the Amazon Resources of your identity (ARN).

If the command succeeds and shows your account information, it means that your profile is properly configured and is ready to use with services like AWS SDKS, AWS CLI, or Badric Agent Core.

Step 2: Install and make your agent

First of all, you need to set up a virtual environment. It prevents conflicts with other projects on your machine.

Let’s create and activate a virtual environment:

On Macos/Linux:

python3 -m venv .venv
source .venv/bin/activate

On Windows (Power Shell or CMD):

python -m venv .venv
.venv\Scripts\activate

Once activated, your terminal prompt will show (.venv) at the beginning. To disable:

deactivate

A requirements.txt File

Create a list of dependence on your project by creating a designated file requirements.txt In the root of the project:

bedrock-agentcore
strands-agents

At the same time it is easy to install everything:

pip install -r requirements.txt

Create a file that says my_agent.py And add the following code:

from bedrock_agentcore import BedrockAgentCoreApp
from strands import Agent

app = BedrockAgentCoreApp()

agent = Agent()

@app.entrypoint
def invoke(payload):
    """Your AI agent function"""
    user_message = payload.get("prompt", "Hello! How can I help you today?")
    result = agent(user_message)
    return {"result": result.message}

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

Break the code

• BedrockAgentCoreApp – Basic run -time rapper that handles, processed and integration with AWS services.

• Agent – A basic agent of the Strands Library Object that can implement and respond to the indicator.

• BedrockAgentCoreApp() The container develops application that manages your agent’s life.

• Agent() Launches a simple sterile agent with default settings. In the case of a real world, you can customize it with specific tools, memory, or reasoning logic.

• @app.entrypoint The decorator marks this function as a black entry point for your agent. Whenever the agent (AWS SDK, CLI, or local tests) are sent, this function is requested.

• Agent is looking for one "prompt" In the coming pay load.

• If no gestures are provided, it is default "Hello! How can I help you today?".

• Agent The Object then takes action on this input and produces the answer.

Step 3: Check the agent locally

Run the agent:

python3 -u my_agent.py

Open another terminal and send a request:

curl -X POST  \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Hello!"}'

If successful, you’ll see:

{"result": "Hello! I'm here to help..."}

You can stop the agent with Ctrl+C.

Step 4: Posted to the Agent Core Run Time

Now you are ready to deploy your agent to AWS.

Create the Agent:

agentcore configure -e my_agent.py

This produces a configuration file called bedrock_agentcore.yaml.

You can begin deployment with this command:

agentcore launch

Will join the output:

Check your deployment agent:

agentcore invoke '{"prompt": "tell me a joke"}'

If you get a joke back, your agent is running successfully.

Step 5: Request Agent with AWS SDK

You can call your agent with the program using Boto3. Create a file that says invoke_agent.py:

import json
import boto3

agent_arn = "YOUR_AGENT_ARN"
prompt = "Tell me a joke"

agent_core_client = boto3.client("bedrock-agentcore")

payload = json.dumps({"prompt": prompt}).encode()

response = agent_core_client.invoke_agent_runtime(
    agentRuntimeArn=agent_arn,
    payload=payload
)

content = ()
for chunk in response.get("response", ()):
    content.append(chunk.decode("utf-8"))
print(json.loads("".join(content)))

Run the script:

python invoke_agent.py

You should see the answer to the AI ​​agent.

Step 6: Clean

If you don’t want to run the agent anymore, delete the run time:

aws bedrock-agentcore delete-agent-runtime --agent-runtime-arn 

Common problems

• Permission refused.: Check your AWS credentials and IAM policies.

• Doer warning: Neglect this unless you use local or local construction.

• Access to the model refused: Enable the model access (such as Claude Swant 4.0) in the Badric Console.

• Make errors: Check Cloud Watch Blood Logs and IAM policies.

Conclusion

The Amazon Bedrock Agent Core makes it easy to build and deploy AI agents without dealing with complex container setup or infrastructure. You can test locally, launch on the cloud with a command, and monitor everything through the cloud watch.

This workflow is ideal for those developers who want to move faster in production from prototypes while staying within the AWS ecosystem.

Resources: