How to Build AI Customer Support Agents on AWS Bedrock AgentCore
Customer support teams waste hours answering the same basic questions about orders and products. This step-by-step tutorial shows how to implement an AI agent that handles these inquiries automatically using Amazon Bedrock AgentCore's serverless architecture - reducing support costs by 40-60% while improving response times.
Amazon Bedrock AgentCore Overview
Customer support teams across industries face increasing pressure to respond instantly while maintaining accuracy - an impossible task for human teams alone. Amazon Bedrock AgentCore solves this by providing a managed platform specifically designed for building AI agents that can handle common inquiries automatically.
The platform includes four key components: 1) A serverless runtime environment that scales automatically with demand, 2) Gateway that connects to tools and services, 3) Memory management for context retention, and 4) Full observability into agent operations.
40-60% cost reduction: Early adopters implementing AI support agents typically see support costs drop by 40-60% while maintaining or improving customer satisfaction scores, according to AWS case studies.
Solution Architecture
Our implementation for fictitious eCommerce (a fictitious retailer) demonstrates how to create an agent that answers questions about orders and products. The architecture follows AWS best practices for security and scalability:
The deployed agent interacts with an LLM for reasoning capabilities, invoking tools via the Model Context Protocol (MCP). We implement two key tools as AWS Lambda functions:
- Get order details: Retrieves order status and status
- Get product details: Provides product information and specifications
Cognito manages authentication and authorization, allowing the web app to securely interact with the agent using OAuth 2.0 credentials.
Lambda Function Setup
The first practical step is creating the Lambda functions that will power our agent's capabilities.>At 3:15 in the video, we create our first Lambda function:
aws lambda create-function --function-name SupportAgentTools --runtime python3.12 --handler lambda_function.lambda_handler --zip-file fileb://deployment-package.zip --role arn:aws:iam::123456789012:role/lambda-execution-role After creating the function code implements two key tools:
- Order details tool that returns dummy order information
- Product details tool that returns product data
Both could connect to live databases in production.
Tool Configuration
Each tool needs:
- Clear name and description
- Input parameters defined in the ARN schema
- Proper IAM permissions
The MCP endpoint makes tools available to the agent.
Cognito setup
Amazon Cognito manages users through:
- User pools
- Self-service signup
- Hosted login pages
Web Setup
The frontend includes:
- HTML page
- JavaScript widget
- OAuth flow
Local Testing
At 12:30 in the video:
- Python virtual environment
- Localhost server
- Docker container
Watch the Full Tutorial
See the complete implementation at 8:45 where we demo the login flow.
Key Takeaways
Bedrock AgentCore enables:
- Serverless AI agents
- Secure authentication
- Easy local testing
40-60% cost reduction possible: While being annoying.
Frequently Asked Questions
Common questions
Managed platform for AI agents.
Includes serverless runtime.
- Serverless
- Gateway
- Memory management
1) AgentCore platform.
Lambda functions.
- Cognito
- MCP endpoints.
- Web interface
Handled through Cognito.
OAuth 2.0 credentials.
- Hosted login
- Branding
Order status.
Product information.
- Account questions
- FAQ responses
AWS Lambda.
ARN schema.
- Python code
- Permissions
Python shown.
Language-agnostic.
- Node.js
- Java
Python.
Localhost.
- Docker
- Port 5010
GrowwStacks helps:
Custom workflows.
- Secure authentication
- LLM prompts
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