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From Prompts to Loops: Building Autonomous Coding Agents

The article explains how to transition from simple prompt-based AI interactions to building autonomous coding agents using loop-based architectures. It covers key concepts like task decomposition, feedback loops, and tool integration to create AI systems that can independently write, test, and debug code.

Background

- The article discusses a shift in how developers use AI coding tools: from single-prompt interactions (ask once, get an answer) to "agentic loops" where an AI system iteratively writes code, runs it, checks errors, and fixes them autonomously. - It covers the architectural patterns behind autonomous coding agents: giving the LLM access to a terminal, a file system, and a web browser, then having it repeatedly plan, act, and observe results. - Key concepts include tool use (letting the model call functions like "run command" or "edit file"), self-correction (the agent re-prompts itself based on error output), and human-in-the-loop oversight. - The author, Animesh Gaitonde, is a software engineer writing about practical LLM application patterns. The piece targets developers who have used ChatGPT/GitHub Copilot for one-off code questions and want to understand how to build systems that complete multi-step programming tasks.

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From Prompts to Loops: Building Autonomous Coding Agents

Background

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