In this hands-on workshop, you’ll build a production-ready Customer Support Agent demonstrating the full power of Amazon Bedrock AgentCore. This sophisticated AI system handles complex customer service workflows, integrates with multiple enterprise systems, and scales to serve thousands of customers simultaneously. Through seven progressive labs, you’ll master the complete lifecycle of enterprise AI agent development: creating agent prototypes, implementing persistent memory, connecting to real customer data through AgentCore Gateway, deploying with enterprise-grade observability, and building customer-facing applications. Working with real-world scenarios from an e-commerce company handling hundreds of daily support requests, you’ll gain in-depth knowledge through Jupyter notebook code samples and architectural explanations.

Most organizations are no longer experimenting with AI - they are trying to scale it. And this is where things start to break. Despite strong models and successful pilots, many AI initiatives fail to deliver real value. Not because the technology doesn’t work, but because success is not well defined, measured and managed in real-world conditions. In this interactive session, participants will step into a simulated AI transformation scenario, making decisions under real constraints such as data limitations, cost pressure, user behavior and governance requirements. Through this exercise, we will uncover why AI systems that “work” in demos often fail in reality and what it actually takes to build systems that are measurable, reliable and worth scaling. Target audience: product managers, data/AI professionals, business leaders, curious minds and anyone involved in implementing or scaling AI systems in practice.

Join us for an interactive, engineering-driven workshop exploring the design of LLM agent systems. Together, we will examine how GenAI applications evolve from simple single-prompt interactions into more structured, agent-based architectures built for reliability, scalability, and more complex workflows. We will unpack key design questions, including when agents truly add value, which types of problems they are best suited to solve, and what trade-offs they introduce in terms of complexity, control, latency, and maintainability. Along the way, we will cover important concepts such as reasoning patterns, tool integration, memory, planning, and orchestration strategies. In a hands-on session, we will also take a deeper look at frameworks such as LangGraph and CrewAI, giving participants the opportunity to explore different implementation approaches in practice and better understand how to design effective agent systems for real-world use cases.