Building an Autonomous Coding Agent with Ollama and React

In the world of AI, "Self-Correction" is the holy grail. It's the difference between a chatbot that gives you a broken snippet and an Agent that finishes the job. Today, we're diving into how we built the Ollama Self-Correcting Coder . The Problem: The "One-Shot" Fallacy Most developers use LLMs in a "one-shot" manner: you ask for code, it gives you something, and if it's broken, you fix it. This is inefficient. A true agent should be able to verify its own work. The Solution: The Reflection Loop Our app implements a recursive loop that mimics the human development process: Code -> Run -> Debug -> Learn . 1. The Execution Sandbox We use the JavaScript Function constructor to execute generated code in real-time. We intercept console.log to capture the agent's "output" and wrap everything in a try/catch block to catch runtime errors. const result = executeCode ( executableCode ); if ( ! result . success ) { // Feed result.error back to the LLM } 2. Persistent Memory (Lessons Learned) The