For developers, building an AI agent prototype is just the first step. The real challenge begins when moving these agents into production, where they need to handle real users, manage complex workflows, and scale reliably. This transition introduces new complexities in tool integration, memory management, and operational visibility. We’ll dive into practical solutions through a developer’s lens, focusing on streamlined implementation of essential production features. Learn how to integrate tools using Model Context Protocol (MCP), architect effective memory systems, and add comprehensive observability to your agent workflows – all while maintaining the flexibility to use your preferred development frameworks.

We’ll demonstrate how Amazon Bedrock AgentCore helps developers bridge the prototype-to-production gap with minimal overhead. Walk away with actionable patterns and code samples that will help you confidently deploy AI agents that can scale from local development to enterprise production.