Frequently Asked Questions
What will I learn in the Generative AI & Agents track?
You’ll learn how to build advanced GenAI systems using LLMs and intelligent agents. Topics include prompt engineering, retrieval-augmented generation (RAG), and MCP for scalable collaboration and orchestration.
Why is agentic design critical for modern GenAI?
Agentic workflows allow LLMs to act autonomously and coordinate with one another. This enables real-world applications like internal copilots, autonomous support agents, and orchestrated task execution at scale.
What is MCP and how does it enable agent collaboration?
MCP (Multi-Agent Communication Protocol) allows agents to securely share memory, context, and goals. It provides the structured communication layer needed for dependable, multi-agent AI systems.
Which use cases are covered?
Topics span customer support, code generation, knowledge retrieval, and document automation. Each example is grounded in production scenarios with real-world LLM infrastructure.