74cbb911c6
* Fix wording typo in notebook about user consent flow cosmetic update Signed-off-by: Hardik Thakkar <68253981+HardikThakkar94@users.noreply.github.com> * Add pyyaml to requirements.txt Signed-off-by: Hardik Thakkar <68253981+HardikThakkar94@users.noreply.github.com> * Add HardikThakkar94 to CONTRIBUTORS.md Signed-off-by: Hardik Thakkar <68253981+HardikThakkar94@users.noreply.github.com> --------- Signed-off-by: Hardik Thakkar <68253981+HardikThakkar94@users.noreply.github.com>
End-to-end Customer Support Agent with AgentCore
In this tutorial we will move a customer support agent from prototype to production using Amazon Bedrock AgentCore services.
What You'll Build
A complete customer support system that starts as a simple prototype and evolves into a scalable and secure sample application.
Your final system will handle real customer conversations with memory, shared tools, and a web interface.
Important
The examples provided here is for educational purposes. It demonstrates how the different services from AgentCore are used on the process of migrating an agentic use case from prototype to production. As such, it is not intended for direct use in production environments.
Journey Overview:
- Start with a basic agent prototype (20 mins)
- Add conversation memory across sessions (20 mins)
- Share tools securely across multiple agents (30 mins)
- Deploy to production with monitoring (30 mins)
- Build a customer-facing web app (20 mins)
Architecture Overview
By the end of the 5 labs of this tutorial you will have created the following architecture
Prerequisites
- AWS account with Bedrock access
- Python 3.10+
- AWS CLI configured
- Claude 3.7 Sonnet enabled in Bedrock
Labs
Lab 1: Create Agent Prototype
Build a prototype of a customer support agent with three core tools:
- Return policy lookup
- Product information search
- Web search for troubleshooting
What you'll learn: Basic agent creation with Strands Agents and tool integration
Lab 2: Add Memory
Transform your "goldfish agent" into one that remembers customers across conversations.
- Persistent conversation history
- Customer preference extraction
- Cross-session context awareness
What you'll learn: AgentCore Memory for both short-term and long-term persistence
Lab 3: Scale with Gateway & Identity
Move from local tools to shared, enterprise-ready services.
- Centralized tool management
- JWT-based authentication
- Integration with existing AWS Lambda functions
What you'll learn: AgentCore Gateway and AgentCore Identity for secure tool sharing
Lab 4: Deploy to Production
Deploy your agent to handle real traffic with full observability.
- Fully managed deployment
- Session Continuity and Session Isolation
- CloudWatch Observability integration
What you'll learn: AgentCore Runtime with production-grade observability
Lab 5: Build Customer Interface
Create a web app customers can actually use.
- Streamlit-based chat interface
- Real-time response streaming
- Session management and authentication
What you'll learn: Frontend integration with secure agent endpoints
Getting Started
- Clone this repository
- Install dependencies:
pip install -r requirements.txt - Configure AWS credentials
- Start with Lab 1
Each lab builds on the previous one, but you can jump ahead if you understand the concepts.
Architecture Evolution
Watch your architecture grow from a simple local agent to a production system:
Lab 1: Local agent with embedded tools
Lab 2: Agent + AgentCore Memory for persistence
Lab 3: Agent + AgentCore Memory + AgentCore Gateway and AgentCore Identity for shared tools
Lab 4: Deployment to AgentCore Runtime and observability with AgentCore Observability
Lab 5: Customer-facing application with authentication
Ready to build? Start with Lab 1 →