This guide provides a practical approach for developers to build their own coding agents from scratch using large language models (LLMs), function calling, retrieval-augmented generation (RAG), code execution and LLM workflows. The goal is to enable developers with the conceptual foundations and code-level insights needed to create their own agent pipelines. By combining tool use, context-aware retrieval, and runtime execution, these agents can go beyond text generation to act as real assistants in software development. The guide covers the key components of a coding agent, including function calling, code retrieval using embedding models, code execution with a safe sandboxed environment, and choosing the right workflow architecture. A real-world example is provided through a data science agent that demonstrates these capabilities in practice.