Building Production-Ready Intelligent Event Agents with Amazon Bedrock AgentCore and Knowledge Bases

Large conferences generate enormous volumes of information—multi-track agendas, speaker profiles, venue maps, and continuously evolving schedules. While basic AI chatbots can answer logistical questions, they rarely provide contextual awareness, personalization, or enterprise-grade scalability.

As an official AWS partner, Softprom helps organizations move beyond prototypes and build secure, production-ready AI solutions. In this article, we explore how to deploy an intelligent event assistant using:

• Amazon Bedrock AgentCore
• Amazon Bedrock Knowledge Bases
• Amazon Cognito

The result: a scalable AI assistant capable of serving thousands of concurrent users with secure authentication, contextual memory, and Retrieval-Augmented Generation (RAG)—without managing infrastructure.

From Prototype to Production: The Real Challenge

Building a demo chatbot is straightforward. Production deployment is not.

Enterprise environments require:

• Secure multi-IDP authentication
• Session isolation and privacy
• Long-term user preference management
• High concurrency support
• Reliable performance under load

Amazon Bedrock AgentCore addresses these challenges with fully managed components that eliminate months of infrastructure engineering.

Solution Architecture Overview

The intelligent event assistant is built on four core capabilities:

1. Secure identity and authentication
2. Serverless runtime with session isolation
3. Short-term and long-term memory
4. Managed knowledge retrieval via RAG

Let’s examine how these components work together.

1. Secure Login and Identity Management

Users authenticate via Amazon Cognito (or other supported IDPs such as Okta or OIDC-compliant providers). After authentication:

• A bearer token is generated.
• The token includes a sub (subject identifier).
• This sub becomes the actor_id for session tracking.

Within Amazon Bedrock AgentCore, the Identity component validates and authorizes each request before agent invocation, ensuring enterprise-grade security and user isolation.

2. Agent Runtime and Session Isolation

At the core of the solution is Amazon Bedrock AgentCore Runtime, which provides:

• Secure, serverless hosting
• Isolated microVM sessions
• Dedicated CPU, memory, and filesystem per session

Each attendee interacts with a logically isolated instance of the agent. This ensures:

• No cross-session data contamination
• Scalable support for thousands of concurrent users
• Consistent performance under high load

This architecture enables personalization at scale without infrastructure management.

3. Intelligent Memory: Context That Evolves Over Time

Short-Term Memory: Conversation Continuity

Short-term memory stores:

• User messages
• Assistant responses
• Session metadata (actor_id + session_id)

This enables contextual follow-ups such as: “What time is that session?”

The agent understands “that session” because it has immediate access to conversation history.

Long-Term Memory: Persistent User Intelligence

Long-term memory extracts and retains meaningful insights across sessions. For example:

• Prefers hands-on workshops
• Interested in serverless and AI topics
• Avoids vendor-led sales sessions

These preferences are automatically extracted using memory strategies and stored in dedicated namespaces for future personalization.

When a returning attendee logs in, the agent retrieves stored preferences and primes itself before the first message.

This transforms a Q&A tool into a continuously learning assistant.

4. Knowledge Retrieval with Amazon Bedrock Knowledge Bases

While memory manages personalization, conferences produce vast structured datasets:

• Session descriptions
• Speaker bios
• Agenda updates
• Venue logistics

Amazon Bedrock Knowledge Bases enables Retrieval-Augmented Generation (RAG) by:

• Ingesting event documentation
• Converting data into vector embeddings
• Performing semantic search
• Returning contextually relevant results

Instead of keyword search, the system retrieves information based on intent and meaning.

Example

User asks: “Which AI sessions should I attend tomorrow?”

The agent:

1. Retrieves session data from the knowledge base.
2. Applies personalization filters from long-term memory.
3. Considers short-term conversation context.
4. Generates tailored recommendations.

The response is factually accurate and personalized.

Orchestration: How It All Works Together

The integration between AgentCore Runtime and Memory is handled through event-driven hooks:

• Agent Initialized Event → Loads long-term preferences.
• Message Added Event → Stores conversation in short-term memory.
• Knowledge Retrieval Tool → Invoked only when necessary.

This selective retrieval model prevents context overload while maintaining precision and speed.

The architecture balances:

• Preloaded personalization
• On-demand knowledge retrieval
• Secure identity validation
• Serverless scalability

Business Impact

By combining:

• Identity management
• Session isolation
• Persistent memory
• Managed RAG

organizations can deploy enterprise-ready AI assistants in days—not months.

The benefits include:

• Reduced infrastructure engineering overhead
• Faster time-to-market
• Secure multi-user environments
• Improved attendee engagement
• Highly personalized digital experiences

Conclusion

Building intelligent conversational agents is no longer the bottleneck. Production deployment is.

With Amazon Bedrock AgentCore and Knowledge Bases, organizations can:

• Deliver personalized, context-aware AI assistants
• Serve thousands of concurrent users securely
• Maintain enterprise-grade authentication
• Eliminate infrastructure complexity

As an AWS partner, Softprom supports organizations in designing and implementing production-ready AI architectures tailored to their business needs.

Whether for conferences, customer portals, or internal knowledge systems, Amazon Bedrock AgentCore provides the managed foundation to deploy scalable, secure, and intelligent agents—transforming prototypes into enterprise solutions.