This content originally appeared on DEV Community and was authored by Seenivasa Ramadurai
Introduction
Welcome back to our AWS AgentCore journey!
we explored what AWS AgentCore is, its modular architecture, and how to build an AI agent (SreeniBot) locally.
Now it’s time to take things up a notch we’re going from local setup to production deployment using AWS Bedrock AgentCore. By the end of this guide, your agent will be running on the cloud scalable, observable, and production-ready.
Prerequisites
Before deploying to AgentCore, make sure you have the following:
AWS Account Setup
- An active AWS account with necessary permissions
- AWS CLI configured with credentials
- Access to AWS Bedrock and AgentCore
Development Environment
- Python 3.8+ installed
- Your local SreeniBot application ready from *Part I *(https://dev.to/sreeni5018/introducing-aws-bedrock-agentcore-a-modular-platform-for-deploying-ai-agents-at-enterprise-scale-369p )
- Basic understanding of containerization
Required IAM Permissions
Your user or role should have access to:
bedrock-agentcore:*
iam:CreateRole, iam:AttachRolePolicy
ecr:CreateRepository, ecr:GetAuthorizationToken
codebuild:CreateProject, codebuild:StartBuild
s3:PutObject, s3:GetObject
logs:CreateLogGroup, logs:CreateLogStream
AgentCore Starter Kit Setup
Step 1: Install the Starter Kit
pip install bedrock-agentcore-starter-toolkit
agentcore --help
Step 2: Verify AWS Configuration
aws configure list
aws bedrock list-foundation-models --region us-east-1
You should see your AWS credentials, region, and profile correctly listed.
Step 1: Initialize AgentCore Configuration
cd /path/to/your/sreenibot-project
agentcore configure \
--name sreenibot \
--entrypoint main.py \
--requirements-file requirements.txt \
--region us-east-1 \
--non-interactive
This command automatically generates configuration files, IAM roles, and an ECR repository for your project.
Deployment Process
Step 1: Launch Your Agent
Deploy your agent to AWS AgentCore:
agentcore launch
Deployment Process Breakdown:
Phase 1: Memory Setup
Creating memory resource for agent: sreenibot
✅ MemoryManager initialized for region: us-east-1
🔎 Retrieving memory resource with ID: sreenibot_mem-PHIirj4zkP...
Found memory: sreenibot_mem-PHIirj4zkP
Found existing memory in cloud: sreenibot_mem-PHIirj4zkP
✅ Using existing STM-only memory
Phase 2: AWS Resources Setup
Starting CodeBuild ARM64 deployment for agent 'sreenibot' to account YOUR-ACCOUNT-ID
Setting up AWS resources (ECR repository, execution roles)...
Getting or creating ECR repository for agent: sreenibot
✅ Reusing existing ECR repository:
[YOUR-ACCOUNT-ID].dkr.ecr.us-east-1.amazonaws.com/bedrock-agentcore-sreenibot
Getting or creating execution role for agent: sreenibot
✅ Reusing existing execution role:
arn:aws:iam::[YOUR-ACCOUNT-ID]:role/AmazonBedrockAgentCoreSDKRuntime-us-east-1-[UNIQUE-ID]
Phase 3: CodeBuild Process
Preparing CodeBuild project and uploading source...
Using dockerignore.template with 45 patterns for zip filtering
Including Dockerfile from .bedrock_agentcore/sreenibot in source.zip
Uploaded source to S3: sreenibot/source.zip
Updated CodeBuild project: bedrock-agentcore-sreenibot-builder
Starting CodeBuild build (this may take several minutes)...
🔄 QUEUED started (total: 0s)
✅ QUEUED completed in 1.1s
🔄 PROVISIONING started (total: 1s)
✅ PROVISIONING completed in 8.8s
🔄 DOWNLOAD_SOURCE started (total: 10s)
✅ DOWNLOAD_SOURCE completed in 2.2s
🔄 BUILD started (total: 12s)
✅ BUILD completed in 9.8s
🔄 POST_BUILD started (total: 22s)
✅ POST_BUILD completed in 6.6s
🔄 FINALIZING started (total: 29s)
✅ FINALIZING completed in 1.1s
🔄 COMPLETED started (total: 30s)
✅ COMPLETED completed in 1.1s
🎉 CodeBuild completed successfully in 0m 30s
Phase 4: AgentCore Deployment
Deploying to Bedrock AgentCore...
Passing memory configuration to agent: sreenibot_mem-[UNIQUE-ID]
✅ Agent created/updated:
arn:aws:bedrock-agentcore:us-east-1:[YOUR-ACCOUNT-ID]:runtime/sreenibot-[UNIQUE-ID]
Observability is enabled, configuring Transaction Search...
CloudWatch Logs resource policy already configured
X-Ray trace destination already configured
X-Ray indexing rule already configured
✅ Transaction Search already fully configured
🔍 GenAI Observability Dashboard:
https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#gen-ai-observability/agent-core
Polling for endpoint to be ready...
Agent endpoint:
arn:aws:bedrock-agentcore:us-east-1:[YOUR-ACCOUNT-ID]:runtime/sreenibot-[UNIQUE-ID]/runtime-endpoint/DEFAULT
Deployment completed successfully!
Testing Your Deployed Agent
Step 1: Basic Functionality Test
Test your deployed agent with a simple message:
agentcore invoke '{"prompt": "Hello SreeniBot! How are you?"}'
Expected Response:
{
"result": "Hello! I'm SreeniBot, your AWS AgentCore agent. You said: 'Hello SreeniBot! How are you?'. I have context from 1 messages in this conversation.",
"session_id": "[SESSION-ID]",
"has_context": true,
"message_count": 2
}
Step 2: Context Memory Test
Test conversation memory by referencing previous messages:
agentcore invoke '{"prompt": "What did I just say?", "session_id": "bef11bb6-a37b-423a-b28e-d0eef9e425fb"}'
Expected Response:
{
"result": "Hello! I'm SreeniBot, your AWS AgentCore agent. You said: 'What did I just say?'. I have context from 3 messages in this conversation.",
"session_id": "[SESSION-ID]",
"has_context": true,
"message_count": 4
}
Step 3: Check Agent Status
Monitor your agent's health and configuration:
agentcore status
Monitoring & Observability
CloudWatch Logs
Monitor your agent's runtime logs:
Follow logs in real-time
aws logs tail /aws/bedrock-agentcore/runtimes/sreenibot-[UNIQUE-ID]-DEFAULT \
--log-stream-name-prefix "[DATE]/[runtime-logs]" \
--follow
View logs from the last hour
aws logs tail /aws/bedrock-agentcore/runtimes/sreenibot-[UNIQUE-ID]-DEFAULT \
--log-stream-name-prefix "[DATE]/[runtime-logs]" \
--since 1h
GenAI Observability Dashboard
Access the comprehensive observability dashboard:
URL: https://console.aws.amazon.com/cloudwatch/home?region=us-east-1#gen-ai-observability/agent-core
Features: Request tracing, performance metrics, error analysis
Data Latency: Up to 10 minutes for initial data appearance
Building Client Apps for Your Deployed SreeniBot
Now let us see how to create various types of client applications that can interact with your deployed AWS AgentCore agent. We'll cover web apps, mobile apps, CLI tools, and API integrations.
Understanding AgentCore Endpoints
Your Deployed Agent Details
From our successful deployment, your SreeniBot has:
{
"agent_name": "sreenibot",
"agent_arn": "arn:aws:bedrock-agentcore:us-east-1:[YOUR-ACCOUNT-ID]:runtime/sreenibot-[UNIQUE-ID]",
"endpoint": "arn:aws:bedrock-agentcore:us-east-1:[YOUR-ACCOUNT-ID]:runtime/sreenibot-[UNIQUE-ID]/runtime-endpoint/DEFAULT",
"region": "us-east-1",
"memory_id": "sreenibot_mem-[UNIQUE-ID]"
}
Invocation Methods
1. Direct AgentCore CLI (for testing):
agentcore invoke '{"prompt": "Hello", "session_id": "optional-session-id"}'
2. AWS SDK (for applications):
import boto3
client = boto3.client('bedrock-agentcore-runtime', region_name='us-east-1')
response = client.invoke_agent(
agentId='sreenibot-[UNIQUE-ID]',
sessionId='your-session-id',
inputText='Hello SreeniBot!'
)
3. HTTP API (if API Gateway is configured):
POST https://your-api-gateway-url/invoke
Authorization: AWS4-HMAC-SHA256 ...
Content-Type: application/json
{
"prompt": "Hello SreeniBot!",
"session_id": "optional-session-id"
}
Authentication & Authorization
AWS IAM Authentication
Your client applications need proper AWS credentials to invoke the AgentCore agent.
- IAM Policy for Client Applications:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"bedrock-agentcore:InvokeAgent",
"bedrock-agentcore:GetAgent"
],
"Resource": [
"arn:aws:bedrock-agentcore:us-east-1:[YOUR-ACCOUNT-ID]:runtime/sreenibot-[UNIQUE-ID]"
]
}
]
}
Web Client
AWS Console
Conclusion
Congratulations! 🎉
You’ve successfully deployed SreeniBot to the AWS AgentCore production environment. Your agent is now:
✅ Cloud-Ready – Running on AWS Bedrock infrastructure
✅ Scalable – Auto-scales based on demand
✅ Observable – With complete monitoring and logging (see it in cloudWatch)
✅ Context-Aware – Retains short-term memory
✅ Production-Grade – Follows AWS best practices
Key Takeaways
- The AgentCore Starter Kit makes deployment fast and repeatable.
- ARM64 containers offer better cost-performance balance.
- Memory configuration drives both functionality and cost efficiency.
- Built-in observability ensures smooth operations.
Thanks
Sreeni Ramadorai
This content originally appeared on DEV Community and was authored by Seenivasa Ramadurai
Seenivasa Ramadurai | Sciencx (2025-10-18T23:25:30+00:00) AWS AgentCore Deployment Guide: Part II – Deploying SreeniBot to Production. Retrieved from https://www.scien.cx/2025/10/18/aws-agentcore-deployment-guide-part-ii-deploying-sreenibot-to-production/
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