Elizabeth Fuentes L for AWS

Posted on Oct 15 • Edited on Nov 5 • Originally published at builder.aws.com

Bring AI agents into production in minutes

#aws #ai #tutorial #tutori

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Elizabeth Fuentes L

AWS Developer Advocate specializing in AI/ML and Generative AI. I simplify complex cloud concepts through hands-on tutorials and real-world examples.

🤯 Production Deployment Challenges for AI Agents

You've built an incredible AI agent. It works well on your laptop.

Now you need to deploy it to production.

Here's what usually happens:

3 weeks setting up infrastructure ⏰
Docker and Kubernetes nightmares 🐳
Security configurations that make you cry 🔐
Scaling policies you barely understand 📈
Session management... what even is that? 🤷

Sound familiar?

Amazon Bedrock AgentCore changes everything.

Deploy production-ready AI agents with just 2 commands. No DevOps degree required. No infrastructure headaches.

This hands-on tutorial shows you exactly how - from local testing to production endpoint in under 15 minutes.

This tutorial is based on Mike Chambers' blog: Turn Your AI Script into a Production-Ready Agent, Thanks Mike :)

🎯 What You'll Build: Production-Ready AI Agent

✅ A calculator AI agent with Strands Agents and Claude as the model provider
✅ Secure APIKey management with AgentCore Identity
✅ Auto-scaling production deployment
✅ Session-aware conversations
✅ Full monitoring and observability

AgentCore Services Overview

Service	Purpose	Key Features
⭐ AgentCore Runtime	Serverless execution	Auto-scaling, Session management, Container orchestration
⭐ AgentCore Identity	Credential management	API keys, OAuth tokens, Secure vault
AgentCore Memory	State persistence	Short-term memory, Long-term storage
AgentCore Code Interpreter	Code execution	Secure sandbox, Data analysis
AgentCore Browser	Web interaction	Cloud browser, Auto-scaling
AgentCore Gateway	API management	Tool discovery, Service integration
AgentCore Observability	Monitoring	Tracing, Dashboards, Debugging

⭐ Used in this tutorial: Runtime and Identity services handle deployment and credential management.

Prerequisites for Amazon Bedrock AgentCore

Before you begin, verify that you have:

AWS Account with appropriate permissions
Python 3.10+ environment
AWS CLI configured with aws configure

New AWS customers receive up to $200 in credits

Start at no cost with AWS Free Tier. Get $100 USD at sign-up plus $100 USD more exploring key services.

Deploy Your AI Agent to Production 🚀

Tutorial Roadmap:

Setup ⚙️ → 2. Code Agent 💻 → 3. Test Locally ✅ → 4. Deploy 🚀 → 5. Invoke ⚡

Estimated time: 15 minutes

Step 1: Create AWS IAM User for AgentCore

Create an AWS IAM user and attach the BedrockAgentCoreFullAccess managed policy.

Step 2: Configure AgentCore Identity for API Keys

Create credential providers through the AgentCore console Identity menu. Store your Claude API key securely using AgentCore Identity's encrypted vault.

AgentCore Identity provides comprehensive credential management with secure storage, OAuth support, and access control across multiple authentication systems.

Step 3: Install Python Dependencies and SDK

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -r requirements.txt

Required packages:

bedrock-agentcore - AgentCore SDK
strands-agents - Agent framework
bedrock-agentcore-starter-toolkit - Deployment toolkit
strands-agents-tools - Calculator functionality

Agent Implementation with Strands Agents Framework

AgentCore Entry Point

The @app.entrypoint decorator makes your agent deployable:

@app.entrypoint
def invoke(payload, context):
    """AgentCore Runtime entry point"""
    agent = create_agent(calculator)

    prompt = payload.get("prompt", "Hello!")
    result = agent(prompt)

    return {
        "response": result.message.get('content', [{}])[0].get('text', str(result))
    }

Secure Credential Management

@requires_api_key(provider_name="ClaudeAPIKeys")
async def retrieve_api_key(*, api_key: str):
    os.environ["CLAUDE_APIKEY"] = api_key

AgentCore Identity retrieves API keys securely without exposing credentials in your code.

Model Configuration

def create_model():
    return AnthropicModel(
        client_args={"api_key": os.environ["CLAUDE_APIKEY"]},
        max_tokens=4000,
        model_id="claude-3-5-haiku-20241022",
        params={"temperature": 0.3}
    )

Performance Optimization

Initialize agents once per session to preserve state and reduce latency:

agent = None
def create_agent(tools):
    global agent
    if agent is None:
        agent = Agent(
            model=create_model(),
            tools=[tools],
            system_prompt="You are a helpful assistant that can perform calculations. Use the calculate tool for any math problems."
        )
    return agent

AgentCore provides session isolation in dedicated containers that run up to 8 hours.

Local Testing Before AWS Deployment

Start your agent:

python3 my_agent.py

Test functionality:

curl -X POST http://localhost:8080/invocations \
  -H "Content-Type: application/json" \
  -d '{"prompt": "What is 50 plus 30?"}'

Deploy AI Agent to AWS Production

Deploy with two commands:

Configure Agent

agentcore configure -e my_agent.py

Provide your IAM role ARN when prompted.

Launch to Production

agentcore launch

AgentCore automatically:

Creates runtime environment
Sets up auto-scaling
Configures security
Provides production endpoint

Verify Deployment

agentcore status

View agent status, endpoint information, and observability dashboards.

You can also monitor deployment progress in the AgentCore console:

Invoke Your Agent

Terminal Testing

agentcore invoke '{"prompt": "What is 50 plus 30?"}' --session-id session-123 --user-id user-456

agentcore invoke '{"prompt": "Now multiply that result by 2"}' --session-id session-123 --user-id user-456

Production Integration

Use AWS SDK for application integration:

import boto3
import json

client = boto3.client('bedrock-agentcore-runtime', region_name='us-west-2')
agent_arn = "arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/your-agent-name"

response = client.invoke_agent_runtime(
    agentRuntimeArn=agent_arn,
    sessionId="production_session_2024_user456",
    inputText="What is 25 * 4 + 10?"
)

result = json.loads(response['body'].read())
print(result['response'])

Production Requirements:

Get Agent ARN from agentcore status
Session IDs must be 33+ characters
Uses AWS credentials for authentication
Supports streaming responses

AgentCore vs Traditional Deployment Comparison

Traditional Deployment	AgentCore Deployment
❌ 3 weeks	✅ 15 minutes
❌ Docker + K8s	✅ Serverless
❌ Manual scaling	✅ Auto-scaling
❌ Complex security	✅ Built-in security
❌ DevOps expertise	✅ 2 commands

Clean Up AWS Resources

Remove all resources:

agentcore destroy

This removes AgentCore deployment, ECR repository, IAM roles, and CloudWatch logs.

🎉 You Just Deployed Your First Production AI Agent!

Now comes the fun part: What will you build? 🚀

💡 Taking It Further

I've been building various AI agents with Strands Agents - from multimodal content processing to multi-agent systems. Now I'm taking them all to production with AgentCore.

If you're curious about what's possible, check out some of the agents I've built:

🎨 Multimodal AI Agents

Process images, videos, and text together:

🤝 Multi-Agent Systems

Agents working together:

🧠 RAG and Memory

Make agents remember and learn:

⚡ Quick Answer

Can you deploy AI agents to AWS production without Docker/Kubernetes expertise?

Yes. Amazon Bedrock AgentCore eliminates infrastructure complexity. Deploy in 2 commands:

agentcore configure -e my_agent.py
agentcore launch

No Docker, no Kubernetes, no manual scaling configuration required.

❤️ If This Helped You

✅ Comment below with your deployment results or questions
❤️ Heart this article to help other developers discover it
🦄 Unicorn it if you successfully deployed in under 15 minutes
🔖 Bookmark for your next AgentCore project
📤 Share with your team on Slack or Twitter