Guide to Agentic AI – Build a Python Coding Agent with Gemini

Understanding Agentic AI and Its Power

Agentic AI represents a fundamental shift in how artificial intelligence systems operate. Unlike traditional one-shot responses where a model generates an answer based on a single input, agentic systems employ iterative loops that allow the AI to reason, plan, and take action over multiple steps. This course demonstrates this distinction early on by showing how an agent can autonomously debug code, suggesting improvements, and implement fixes—tasks that would require human intervention in conventional AI applications. The core strength of agentic AI lies in its ability to use tools, gather feedback, and adjust its approach dynamically, making it far more capable for real-world software development tasks.

The Foundation: Tool Calling and Agentic Loops

At the heart of every functional AI agent is the concept of tool calling, a mechanism that allows language models to declare and invoke functions to accomplish tasks. The course structures this learning around four essential tools that enable an AI system to interact meaningfully with a codebase: reading file information, retrieving file content, writing files, and executing Python code. These tools form the backbone of the agent's ability to perceive its environment, understand problems, and take corrective action. The agentic loop itself is elegantly simple—the agent receives a user request, decides which tools to use, executes them, processes the results, and repeats until the task is complete or the goal is achieved.

Setting Up the Python Development Environment

The course begins with practical setup guidance, using UV as the Python project manager to streamline dependency management and environment configuration. This modern approach reduces friction and ensures reproducibility. Participants then integrate the Gemini Flash API, Google's efficient language model optimized for speed and cost-effectiveness. Making the first API call introduces the mechanics of sending requests and handling responses, establishing the foundation for all subsequent agent interactions. The setup phase also covers command-line argument handling and conversation history management, essential for building a robust conversational interface that maintains context across multiple exchanges.

Implementing Core Agent Tools

The tutorial walks through implementing each tool systematically. The first tool, Get Files Info, enables the agent to explore directory structures and understand what files exist in a project. The second tool, Get File Content, allows the agent to read the actual code within those files, necessary for understanding the codebase structure and identifying problems. The third tool, Write File, grants the agent the ability to create or modify files, implementing fixes or generating new code. Before implementing the fourth tool, the course addresses a critical security concern: the dangers of running arbitrary AI-generated code. This discussion is vital for anyone deploying agents in production environments, as unvetted code execution poses substantial risks. The fourth tool, Run Python File, allows the agent to execute code and receive feedback on success or failure, completing the feedback loop that drives autonomous problem-solving.

Declaring Functions for the Language Model

A crucial concept in tool-based agents is function declaration—explicitly telling the language model what functions are available, what parameters they accept, and what they do. This declarative approach allows the model to understand its capabilities and choose appropriate tools when reasoning about a problem. The course demonstrates how to structure these declarations in a format compatible with the Gemini API, ensuring the model can reliably invoke tools. The system prompt plays a parallel role, providing the agent with context about its role, constraints, and objectives. A well-crafted system prompt significantly influences agent behavior, guiding it toward productive problem-solving while maintaining alignment with user intentions.

Building the Function Calling Logic

Once tools are declared and the system prompt is established, the next step is implementing the logic that processes the model's function calls. This involves parsing the model's output, identifying which function it wants to call, extracting parameters, executing the function, and feeding results back into the agent loop. The course walks through this implementation step-by-step, showing how to handle edge cases and ensure robust execution. This is where the agentic loop truly comes alive—the agent can now perceive, decide, and act in a continuous cycle, each iteration building on the previous one's results.

Creating the Complete Agentic Loop

The agentic loop ties everything together: user input enters the system, the agent processes it using the language model and available tools, actions are taken in the codebase, feedback is collected, and the agent reassesses the situation. The final demo illustrates this in action, showing an AI agent autonomously detecting a bug in a calculator application, understanding the root cause, implementing a fix, and verifying the correction works. This demonstration powerfully illustrates the value of agentic systems—what might take a developer several manual steps happens automatically through reasoning and tool use.

Practical Applications and Next Steps

The skills taught in this course extend far beyond the specific calculator project. Understanding how to build agentic systems opens doors to autonomous code generation, bug fixing, documentation generation, and complex multi-step problem-solving. The foundation built here—tool design, function declaration, loop architecture, and prompt engineering—applies across countless use cases in software development and beyond. Participants completing this course gain not just working code but deep comprehension of how modern AI tools operate internally, positioning them to build and customize agents for their own projects and workflows.