Deploying a Production-Ready Event Assistant Using Amazon Bedrock AgentCore
Transforming Conference Navigation with AI
Introduction to Event Assistance Challenges
Building an Intelligent Companion with Amazon Bedrock AgentCore
Solution Architecture
How the Solution Works
- User Login and Identity Retrieval
- Agent Invocation and Initialization
- Message Processing
- Knowledge and Memory Retrieval
- Response Generation
Solution Components
The Agent: Runtime and Identity Integration
Agent Memory Strategies
- Short-Term Memory: Capturing Conversations
- Long-Term Memory: Building Persistent Intelligence
- Agent and Memory Orchestration
Amazon Bedrock Knowledge Bases
Conclusion
Summary of Deployment Benefits
The Evolution of Event Assistance
Next Steps
Enhancing Capabilities and Exploring Resources
About the Authors
- Dani Mitchell: Senior Generative AI Specialist
- Sergio Garcés Vitale: Senior Solutions Architect
- Akarsha Sehwag: Generative AI Data Scientist
Building a Personalized Event Assistant with Amazon Bedrock AgentCore
Large conferences and events can often feel overwhelming, with a flood of information coming your way—from numerous workshops and sessions to speaker profiles, venue maps, and dynamic schedules. While basic AI assistants may address simple queries about event logistics, they often fall short in delivering the personalized, contextual guidance that attendees truly need. Transitioning from prototype to a full-fledged production system, especially one that meets enterprise standards for security and scalability, can require significant time and resources.
In this post, we will demonstrate how to swiftly deploy an intelligent event assistant using Amazon Bedrock AgentCore components. This assistant will remember attendee preferences and provide personalized experiences over time, all while ensuring robust enterprise-grade performance.
Getting Started with Amazon Bedrock AgentCore
Framework Overview
Before diving deeper, let’s understand the components you’ll utilize to create your production-ready assistant:
- Amazon Bedrock AgentCore Memory: Maintains conversation context and long-term preferences without the need for separate storage solutions.
- Amazon Bedrock AgentCore Identity: Enables secure multi-identity provider (IDP) authentication.
- Amazon Bedrock AgentCore Runtime: Provides serverless scaling and safeguards session isolation.
- Amazon Bedrock Knowledge Bases: Facilitates managed retrieval and augmented generation of event data.
By the end of this post, you will be able to deploy an event assistant that enhances its usefulness with each interaction, ensuring attendees effortlessly discover valuable sessions while supporting thousands of concurrent users.
Solution Architecture
Let’s explore how our intelligent event assistant is structured and how it operates. The complete implementation can be found in the GitHub repository, which offers a guided notebook for deploying this solution in your AWS account.
How the Solution Works
1. User Login and Identity Retrieval
Attendees log in through Amazon Cognito (which also supports various IDPs like Okta and Auth0). Upon successful authentication, a bearer token is generated, containing user information used for authenticating subsequent interactions.
2. Agent Invocation and Initialization
When a user submits a query, this triggers the Amazon Bedrock AgentCore Runtime, which utilizes the bearer token and session ID to authenticate requests. During the first interaction, the assistant initializes its context by retrieving user preferences from Amazon Bedrock AgentCore Memory.
3. Message Processing
Messages are stored in short-term memory, organized by user ID and session ID. These interactions are then processed to extract valuable insights, such as user preferences, which are stored for future reference. Each session runs in isolated environments, preventing data contamination across users.
4. Knowledge and Memory Retrieval
To address user requests effectively, the agent can fetch data from Amazon Bedrock’s knowledge base to provide up-to-date details about sessions, schedules, and speaker profiles.
5. Response Generation
The agent synthesizes a response taking into account three layers of context: insights from long-term memory, recent short-term memory messages, and current event data. This holistic approach transforms a simple query into a highly personalized experience.
Solution Components
Agent Infrastructure
At the heart of our solution is Amazon Bedrock AgentCore Runtime which offers a secure, serverless environment for our agent to operate. Each interaction runs in isolated microVMs, enabling thousands of attendees to engage simultaneously without data cross-contamination.
Agent Memory
Amazon Bedrock AgentCore Memory is crucial for providing context awareness. It comprises short-term memory, capturing conversation flow, and long-term memory, which retains user preferences and other meaningful insights that evolve over time.
Knowledge Bases
Structured information about the event—session details, speaker profiles, and logistics—are organized and indexed using Amazon Bedrock Knowledge Bases. This allows for semantic searching and retrieval of relevant data based on meaning rather than keywords.
Conclusion
This post illustrates the process of leveraging Amazon Bedrock AgentCore components to transform an event assistant prototype into a full-fledged, enterprise-ready deployment. The integrated services handle many complex aspects around authentication, scaling, memory management, and RAG capabilities, drastically reducing the time and effort needed for infrastructure development.
By remembering user interests and preferences over time, the assistant fosters an ongoing and intuitive conversation with attendees. Whether you’re organizing a small meeting or a large conference, Amazon Bedrock AgentCore equips you with the tools to offer personalized assistance in days rather than months.
Next Steps
If you’re ready to take your event assistant to the next level, consider the following enhancements:
- Expand Capabilities with AgentCore Gateway: Connect to additional tools and services to enrich your assistant’s functionality.
- Explore the GitHub Repository: Dive into the complete implementation for step-by-step guidance and code examples.
About the Authors
Dani Mitchell is a Senior Generative AI Specialist Solutions Architect at AWS, focused on advancing enterprises’ generative AI capabilities.
Sergio Garcés Vitale is a Senior Solutions Architect at AWS with over a decade of experience in telecommunications and cloud adoption.
Akarsha Sehwag is a Generative AI Data Scientist for the Amazon Bedrock AgentCore GTM team, specializing in enterprise solutions across various AI domains.
Feel free to reach out or check the GitHub repository to start enhancing your event experiences!