Building Foundational AI Agents from Scratch

AI agents are transforming how we interact with technology by moving beyond simple chat prompts to autonomous execution. Understanding how these agents think, plan, and use tools is the first step toward building next-generation software. In this text-based course, you will transition from a curious learner to a builder of functional, text-based AI agents. You will understand the architectural patterns that allow language models to reason, make decisions, and interact with external data sources. What you'll learn: - Understand the fundamental architecture of autonomous AI agents and how they differ from standard language model prompts. - Implement the Reason and Act framework to help your agents plan and execute multi-step tasks. - Configure basic tools and function calling to connect your agents to external data sources and APIs. - Apply prompt engineering strategies to guide agent behavior, memory retention, and decision-making logic. - Design simple retrieval-augmented generation patterns to ground agent responses in specific text documents. - Practice debugging agent loops and handling edge cases where reasoning paths go off course. You will start by exploring key terminology and the foundational concepts of agentic workflows. From there, you will read through step-by-step logic breakdowns, analyze clean code snippets, and complete written exercises that guide you through designing decision-making loops and tool integration. This course is designed for software developers, tech enthusiasts, and curious beginners who want to understand the mechanics of AI agents. No prior experience with AI frameworks is required, though a basic familiarity with programming logic is helpful. Start reading today to unlock the skills needed to design your own autonomous digital assistants.