Loop Engineering: The Framework Making AI Agents More Reliable

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Developer Rijul, creator of the open-source AI code reviewer git-lrc, argues that AI agents are fundamentally built on structured feedback loops rather than sophisticated magic. Unlike traditional chatbots that rely on back-and-forth prompting, agents are designed to autonomously execute tasks by repeating cycles of action, review, and correction. A emerging concept called Loop Engineering focuses on designing these cycles deliberately, going beyond prompt engineering to include actions, feedback mechanisms, memory, and stopping conditions. For an agent to work reliably, it must know not only what to do but also how to verify results and recognize when a task is complete. Without well-defined stop conditions and feedback signals, agents risk running indefinitely without ever confirming success.

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