Data Cloud Foundations for AI Agents and Retrieval Augmented Generation

Modern businesses are increasingly relying on autonomous AI agents to interact with customers and automate tasks, but these agents are only as good as the data they can access. To perform reliably, agents require real-time, secure, and context-rich enterprise data without compromising privacy or accuracy. This text-based course guides you through using Data Cloud to feed, guide, and protect your AI systems. You will start by mastering foundational concepts of data virtualization, semantic layers, and how unstructured data is ingested. Then, you will progress to connecting this data to AI agents using Retrieval Augmented Generation (RAG) and establishing robust guardrails to prevent hallucinations and secure sensitive information. What you'll learn: - Understand the core architecture of Data Cloud and how it unifies enterprise data for AI systems - Configure Retrieval Augmented Generation (RAG) to feed relevant, real-time context to AI agents - Implement secure AI guardrails to enforce data privacy, compliance, and brand safety - Design semantic search and vector database queries to retrieve high-quality unstructured data - Apply data governance best practices to control what information agents can access and share - Analyze agent performance metrics to continuously optimize response accuracy and trust This course is structured for step-by-step reading. You will begin with essential terminology and data models before exploring practical integration patterns, security configurations, and real-world deployment scenarios. This course is designed for beginners, data analysts, and system administrators who want to understand the intersection of cloud data and AI agents. No prior experience with AI engineering or machine learning is required. Start reading today to build the secure, data-driven foundation your AI agents need to succeed.