Agentic AI Workflows for Data Practitioners

As data environments become increasingly complex, traditional automation often falls short of modern operational demands. Agentic AI enables data teams to build self-directing systems that can reason, select appropriate tools, and collaborate to solve complex data challenges dynamically. This text-only course guides you from the absolute basics of autonomous systems to designing workflows that automate data analysis, synthesis, and reporting. By completing this course, you will understand how to transition from static language model prompts to active, goal-driven AI agents that can interact with databases and execute complex tasks. You will gain the conceptual framework needed to integrate agentic design patterns into your daily data workflows. What you'll learn: - Understand the core concepts of agentic AI, including autonomy, planning, and memory architectures. - Configure AI agents to interact with databases and external tools using modern function-calling techniques. - Design structured multi-agent workflows where specialized agents collaborate to solve complex data tasks. - Apply retrieval-augmented generation (RAG) to ground agent decisions in verified private data. - Implement best practices for monitoring, evaluating, and securing agentic data pipelines. This course begins with foundational definitions of agency and language model reasoning before guiding you through structured written tutorials on designing single-agent and multi-agent architectures. This text-based learning format allows you to study at your own pace, focusing deeply on system design and logic. This course is designed for data analysts, engineers, and technology practitioners who want to leverage advanced AI automation; no prior experience with machine learning or agentic frameworks is required. Start reading today to transform your data operations with autonomous AI agents.