Building and Evaluating Data Agents

Modern data analysis requires more than simple search queries; it demands autonomous systems that can plan, execute, and verify their own analytical steps. This course guides you through the foundational concepts and practical architectures of data-driven AI agents. You will discover how to construct single-purpose agents, coordinate them into multi-agent networks, and establish robust testing pipelines to ensure their outputs are accurate and safe. By the end of this text-only course, you will be able to design autonomous systems that connect to databases, execute complex reasoning paths, and self-correct when they encounter errors. What you'll learn: - Understand the core architecture of LLM-based data agents and planning loops - Connect agents securely to external databases, APIs, and document stores - Coordinate multi-agent systems with clear roles, handoffs, and shared memory - Evaluate agent performance systematically using modern testing frameworks - Implement guardrails and safety protocols to prevent hallucinations and loops - Optimize agent efficiency using prompt engineering and structured output formats We begin with basic agent definitions and simple planning patterns, then progress step-by-step through database integration, multi-agent communication, and automated evaluation strategies. Each concept is reinforced with clear written explanations and structured code snippets. This course is designed for software developers, data analysts, and AI enthusiasts who want to move beyond basic prompting. No prior experience with agentic workflows is required; a basic understanding of Python is recommended. Start reading today to build and deploy reliable, autonomous data agents.