How Does Claude Code Actually Work? Let’s Make Our Own

Understanding the Agent Loop

The video opens with a fundamental question: what actually happens when a developer interacts with Claude Code? The answer lies in the agent loop, a continuous cycle where the model receives input, decides on actions, executes those actions through tools, and observes results. This loop repeats until the task resolves. The loop's design determines whether the agent feels autonomous or chaotic, and understanding it is the first step toward building a capable coding assistant.

The Five Core Building Blocks

The presenter breaks Claude Code into five essential building blocks. These include the underlying model, the tools it can call, memory to persist information, context management to decide what the model sees, and guardrails to prevent destructive actions. Each block plays a distinct role, and their interaction creates the experience of a reliable coding agent. The video emphasizes that no single component is mysterious; together they form a coherent system.

How the Model Chooses Tools

A central theme is how an LLM decides which tools to call. The model does not have intrinsic knowledge of file systems or terminal commands. Instead, it receives tool definitions in its context, each describing a name, parameters, and purpose. When the model generates a response, it can emit a structured tool call request. The system then executes the corresponding function and returns the result. This mechanism turns a general text generator into an actionable agent.

Context Window Management

Managing context is critical for long coding sessions. The video explains how the context window fills with system prompts, conversation history, file contents, and tool outputs. Without careful management, the model quickly loses track of early decisions. Techniques include truncating old messages, summarizing completed steps, and selectively loading relevant file portions. These strategies keep the agent focused and reduce token costs while maintaining coherence across complex tasks.

Guardrails and Safety

Guardrails prevent the agent from making harmful or irreversible changes. Examples include confirmation prompts before deleting files, restrictions on executing certain commands, and limits on writing outside the project directory. The video demonstrates how a simple guardrail system can block dangerous actions while still allowing productive work. This layer is essential for trust, especially when running terminal commands autonomously.

Building a Mini Claude Code

The final portion of the video shifts from theory to practice. The presenter assembles a minimal working version of Claude Code using Python. The implementation includes a basic agent loop, a set of tools for reading and editing files, and a context manager. The demo shows the mini agent performing real coding tasks. This hands-on approach solidifies the concepts and shows that the core mechanics are accessible to any developer willing to experiment.

Implications for Developers

Understanding coding agents from the inside changes how developers use them. It reveals why they sometimes fail, why context matters, and how to design better prompts. It also opens the door to building custom agents tailored to specific workflows. The video positions this knowledge as a competitive advantage, not just for building tools but for using existing ones more effectively.