How to Build a Coding Agent from Scratch: A Practical Guide for Developers
Blog post from Together AI
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.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 6 | 4,633 | 712 | 227 | -3% |
| Vector Search | 6 | 1,744 | 310 | 142 | -4% |
| RAG | 2 | 1,320 | 205 | 95 | +23% |
| AI Coding Assistant | 1 | 886 | 151 | 87 | +4% |
| Developer Experience | 1 | 912 | 338 | 116 | +97% |
| Real-time | 1 | 5,311 | 1,306 | 270 | +26% |
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