Building an Autonomous Coding Assistant: A LangGraph.js Capstone Guide

The dream of autonomous software engineering is no longer science fiction. It's a practical architectural challenge. Instead of asking an AI to "write code," we are now building systems that can perceive a codebase, plan a multi-step implementation, execute terminal commands, and iteratively debug their own work. This is the shift from simple chatbots to true agentic workflows. In this capstone guide, we will dissect the architecture of an autonomous coding assistant. We will explore how to move beyond monolithic LLM calls to a system of specialized agents—Planners, Coders, and Testers—orchestrated via LangGraph.js. By the end, you will understand how to build a self-correcting loop that mimics the workflow of a human developer. The Architecture of Autonomy: From Monoliths to Microservices To build a robust autonomous agent, we must abandon the "one-shot" prompt approach. Asking a single Large Language Model (LLM) to "write a bug-free Python script" is akin to asking a single developer