AI Agents: Implementation and Workflow Automation

Artificial intelligence is evolving from simple chatbots into autonomous agents capable of planning and executing multi-step tasks. This shift requires a new understanding of how to structure workflows and manage AI reasoning to achieve reliable results. In this course, you will transition from basic prompting to understanding the logic behind sophisticated agentic systems that can interact with data and tools. You will learn the core principles of autonomous decision-making and how to maintain control over AI outputs in professional environments. What you'll learn: - Understand the core architecture and terminology of modern AI agents. - Apply prompt engineering techniques to improve agent reasoning and planning. - Implement Retrieval-Augmented Generation (RAG) patterns for data-aware agents. - Configure agents to use external tools and APIs for task completion. - Practice designing agentic workflows for complex problem-solving. - Explore the role of vector databases in providing agents with long-term memory. The course starts with foundational definitions and the history of agentic behavior before guiding you through the practical logic of configuring autonomous systems. You will read through detailed explanations and analyze written examples to understand how these systems function. This course is designed for beginners who want to understand the next generation of AI technology. No prior experience with AI implementation is required to get started. Begin your journey into the world of autonomous AI agents today.