How the ReAct Loop Turns a Code Generator into a Real Coding Agent

A developer tutorial series on DEV Community explains the shift from single-call code generation to a multi-step ReAct (Reason, Act, Observe) loop for AI coding agents. Earlier in the series, an agent received a spec and tests in one prompt and returned a complete function, a method that breaks down when tasks involve large repositories with thousands of files. The ReAct approach instead equips the agent with tools — such as reading files, searching patterns, writing, and running commands — so it can gather its own context dynamically rather than relying on a pre-loaded prompt. The core loop remains the same (state, action, observation, stop condition), but the action is now a choice among several tools rather than a single code-writing step. This distinction, the author argues, is what separates a basic function generator from an agent capable of operating meaningfully on a real codebase.