Transforming AI Prototypes into Production-Ready Systems with Amazon Bedrock AgentCore

The Customer Support Agent Journey

Solution Overview

Create the Agent

Test the Proof of Concept

The Proof of Concept Reality Check

Add Persistent Memory for Hyper-Personalized Agents

Centralize Tools with Amazon Bedrock AgentCore Gateway and Amazon Bedrock AgentCore Identity

Deploy to Production with Amazon Bedrock AgentCore Runtime

Create a Customer-Facing UI

Conclusion

About the Authors

Building a Production-Ready AI Agent with Amazon Bedrock AgentCore

Building an AI agent that effectively handles real-life use cases in a production environment is no small feat. Transitioning from a proof of concept (PoC) to a production-ready system requires addressing critical issues such as scalability, security, observability, and operational concerns that often remain hidden during development. This blog post explores how Amazon Bedrock AgentCore facilitates this transition, focusing on a customer support agent that evolves from a simple prototype into an enterprise-grade solution.

The Customer Support Agent Journey

Customer support is one of the most compelling use cases for agentic AI. Businesses today manage thousands of customer inquiries daily, from simple questions to complex troubleshooting. Traditional methods like rule-based chatbots fall short, while human-only support teams struggle with scalability and consistency. An intelligent customer support agent must efficiently handle diverse scenarios while adhering to security and reliability standards.

Typical Evolution Path for Customer Support Agents

1. Proof of Concept (PoC) Stage: Teams create a simple local prototype that can handle basic inquiries. While successful for demos, it lacks the robustness required for real interactions.

2. Reality Check: Scaling challenges emerge as soon as multiple users attempt to access the system simultaneously. Issues such as memory loss, performance monitoring gaps, and security vulnerabilities surface.

3. Production Challenge: Moving to production involves tackling session management, secure tool sharing, observability, and user-friendly interfaces. Often, promising PoCs stall at this stage due to the complexities involved.

Solution Overview

We begin with the creation of a functional prototype using Strands Agents, an open-source agent framework, and Anthropic’s Claude 3.7 Sonnet via Amazon Bedrock. This initial stage incorporates three essential capabilities for customer inquiries:

1. Return Policy Lookup: Provides structured policy information based on product categories.
2. Product Information Retrieval: Delivers technical specifications and warranty details.
3. Web Search for Troubleshooting: Allows real-time access to updated technical solutions.

For the detailed implementation, including the end-to-end code, visit our GitHub repository.

Create the Agent

Here’s a basic outline of our agent code:

from strands import Agent
from strands.models import BedrockModel

@tool
def get_return_policy(product_category: str) -> str:
    # Dummy implementation for return policy lookup
    return {"return_window": "10 days"}

agent = Agent(
    model=BedrockModel(model_id="us.anthropic.claude-3-7-sonnet-v1"),
    tools=[get_return_policy],
    system_prompt="You are a helpful customer support assistant."
)

Testing the PoC

Upon testing the prototype with realistic queries, our agent demonstrates successful tool selection and interaction:

response = agent("What's the return policy for my ThinkPad X1 Carbon?")
# Correct tool selection for return policy inquiry

response = agent("My iPhone 14 heats up, how do I fix it?")
# Correctly uses web search for troubleshooting

Reality Check Highlights

While our PoC demonstrates potential, it exposes several limitations:

• Memory Loss: The agent loses context when sessions end, disrupting user experience.
• Limited Concurrent Users: The agent can only support one conversation at a time.
• Embedded Tools: Modifying tools requires changing agent code and redeploying.

These issues can stall deployments, prompting the need for Amazon Bedrock AgentCore services.

Enhancing with Amazon Bedrock AgentCore Memory

To mitigate memory loss, we implement Amazon Bedrock AgentCore Memory, which provides:

• Short-term Memory: For immediate context within interactions.
• Long-term Memory: For capturing customer preferences across conversations.

Installation and Setup

We begin by installing the necessary dependencies:

pip install boto3 bedrock-agentcore bedrock-agentcore-starter-toolkit

Implementation

Here’s how we can create memory resources:

from bedrock_agentcore.memory import MemoryClient

memory_client = MemoryClient(region_name="us-west-2")
response = memory_client.create_memory_and_wait(
    name="CustomerSupportMemory",
    strategies=[USER_PREFERENCE, SEMANTIC]
)

Integration with the Agent

Next, we integrate the memory functionality with our agent, creating hooks for automatic context retrieval and saving conversation data.

Benefits of AgentCore Memory

1. Conversation Continuity: Customers can pick up where they left off.
2. Personalized Service: Recommendations based on past interactions.
3. Contextual Troubleshooting: Access to previous problems leads to more effective support.

Centralizing Tools with Amazon Bedrock AgentCore Gateway

To address tool architecture issues, we implement Amazon Bedrock AgentCore Gateway, centralizing tools into secure endpoints for reuse.

Integration Steps

1. Convert existing tools to Lambda Functions: For example, turning the web search functionality into an MCP endpoint allows for seamless integration.
2. Configure Security: Using Amazon Bedrock AgentCore Identity, we implement OAuth for secure access.

Enhanced Agent Functionality

With centralized tools, our agent can now leverage enterprise capabilities, maintaining high security and facilitating maintenance.

Deploying to Production with Amazon Bedrock AgentCore Runtime

The final stage involves transitioning our agent from local to production-ready using Amazon Bedrock AgentCore Runtime.

Minimal Code Changes for Production

Transitioning requires only a few lines of code:

from bedrock_agentcore_starter_toolkit import Runtime

agentcore_runtime = Runtime()
launch_result = agentcore_runtime.launch()

Secure Deployment

Set up authentication via Amazon Cognito and utilize Docker for containerization, ensuring a scalable endpoint.

Session Management

Automatic session isolation guarantees that customer interactions remain independent, addressing one of the critical challenges in production environments.

Creating a Customer-Facing UI

Finally, we create a Streamlit-based web application, providing an intuitive chat interface for customers. This web app integrates seamlessly with our production environment.

Conclusion

Our journey illustrates how Amazon Bedrock AgentCore services can systematically overcome traditional barriers, transforming prototypes into robust, scalable applications. The integration of memory capabilities, centralized tool management, runtime deployment, and observability ensures enterprises can develop customer support solutions that effectively meet today’s demands without extensive infrastructure development time.

Ready to build your own production-ready agent? Dive into our complete tutorial and explore additional use cases to get started.

About the Author

Maira Ladeira Tanke is a Tech Lead for Agentic AI at AWS, specializing in autonomous AI systems. With over 10 years of experience in AI/ML, Maira helps organizations harness the power of agentic applications using Amazon Bedrock AgentCore.

For more information and resources, visit Amazon Bedrock AgentCore documentation.