Traditional AI chatbots face challenges when scaling: session management complexity, memory persistence overhead, and limited observability. Amazon Bedrock AgentCore changes this paradigm with purpose-built infrastructure for AI agents.

In this session, you'll learn how to transform a traditional RAG chatbot into a robust AI agent using AgentCore's key services. We'll explore:

• Agentic RAG - Moving from static retrieval to dynamic tool-calling patterns
• Session isolation - How dedicated containers per user session simplify state management and enhance security
• Managed memory - Automatic conversion of short-term conversations into long-term insights and user preferences
• Serverless vector storage - Eliminating database overhead with S3 Vectors
• Built-in observability - End-to-end tracing from user interactions to LLM calls

Through a demo and walkthrough of the AI Agent Accelerator reference implementation written in Python and the Strands Agent framework, you'll see how AgentCore Runtime enables 8-hour agent sessions, how AgentCore Memory eliminates database complexity, and how S3 Vectors provides truly serverless vector storage.

Walk away with a GitHub repository containing the complete reference architecture and deployment scripts to deploy scalable AI agents on AWS.