Course overview
This course provides a hands-on introduction to agentic AI, moving from theory to practice by guiding you through building a Python coding agent. You will learn how to construct autonomous systems that leverage the Gemini Flash API to reason, plan, and execute multi-step tasks. The project focuses on implementing core agent components such as agentic loops, function declarations, and tool calling for file interaction and code execution. You will design system prompts that guide an LLM's decision-making and build feedback loops that allow the agent to self-correct, distinguishing this workflow from a standard one-shot AI response.
Who this course is for
This course is designed for developers with Python experience who want to understand and build agentic AI systems. It is ideal for software engineers exploring the practical application of large language models beyond simple chat interfaces. If you are curious about how AI can autonomously debug, refine, and execute code without constant human prompting, this content will show you a concrete implementation. Familiarity with command-line tools and fundamental API concepts will help you follow the project as you construct a functional, autonomous coding assistant from scratch.
How to study this sequence
Focus on the single, comprehensive project lesson that builds the Python coding agent sequentially. Follow along by writing the code to implement each component, from initializing the Gemini Flash API to adding tools for file interaction. Pay close attention to the construction of the agentic loop and the function declarations, as these are the mechanisms enabling autonomous decision-making. After completing the project, experiment by modifying the system prompts and adding new tools to observe how the agent's behavior changes. The recommended next step is to explore advanced architectures like ReAct.
What you should be able to do
Upon completing the project, you will have a functional AI agent built with Python and the Gemini Flash API. You will understand how to design and implement agentic loops, configure function declarations, and orchestrate tool calling for tasks like code generation and debugging. You will gain practical experience in creating autonomous workflows that iterate and self-correct, forming a solid understanding of the foundational patterns that underpin more complex, multi-agent systems.
