{"slug": "ai-agents-are-not-magic-they-are-just-good-feedback-loops", "title": "AI Agents Are Not Magic. They Are Just Good Feedback Loops", "summary": "Rijul, developer of git-lrc, argues that AI agents are not magic but well-designed feedback loops. He introduces 'Loop Engineering' as a methodology for building reliable agent workflows by focusing on actions, feedback, and stop conditions rather than just prompt engineering.", "body_md": "*Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free and source-available on GitHub. Star git-lrc to help more developers discover the project. Do give it a try and share your feedback*\n\nPast few years, when things like GPT came out, we were mainly learning how to use them.\n\nWhich basically meant using better prompts.\n\nSo we were continuously refining prompts and trying to get better outputs.\n\nAnd that is fine.\n\nThis was basically an exchange back and forth between us and the bot.\n\nWe ask something.\n\nThe AI responds.\n\nWe refine our prompt.\n\nThe AI responds again.\n\nThis loop continues.\n\nBut now we are seeing the rise of AI agents.\n\nWe see them everywhere, in every LinkedIn post, every YouTube video.\n\nThe main idea behind agents is to hand over our work to them, and they will handle the remaining tasks with less constant back and forth compared to traditional chatbots.\n\nHere, agents are expected to work in loops.\n\nThey do one thing.\n\nThen they review the result, identify additional things that need to be done, take action again, and repeat.\n\nThe main input we give is usually a goal or a prompt.\n\nBut this alone will not give us better control over how the agent works.\n\nReliable agents also need things like success criteria, access to the right tools, feedback mechanisms, and rules for when to stop or ask for human input.\n\nThe agent works in loops.\n\nSo we need a better way to design and control these loops.\n\nAgents perform tasks through loops.\n\nSo instead of only focusing on prompt engineering, we also need to engineer the loop in which the agent operates.\n\nThis is where Loop Engineering comes in.\n\nThe term has recently gained attention as people started exploring better ways to build reliable workflows around AI agents.\n\nThe idea is simple:\n\nInstead of only telling an agent what to do, we design the process that helps the agent complete the task reliably.\n\nPrompting is still an important part of this process.\n\nBut it becomes one layer in a bigger system that includes actions, feedback, memory, verification, and stopping conditions.\n\nThere are many ways to think about designing agent loops.\n\nA simple way to understand them is through three important parts:\n\nActions are anything that an agent needs to perform.\n\nTake us humans as an example.\n\nWhen we build software, we write code, read code, interact with files, create new files, run commands, and test our changes.\n\nThese are actions.\n\nSimilarly, for an agent, actions are the things it can perform using its available tools.\n\nExamples:\n\nWithout actions, an agent has limited ability to interact with the world and becomes closer to a traditional chatbot.\n\nJust performing actions is not enough.\n\nAs humans, we don't just complete a task and assume it is correct.\n\nWe verify our work and look for feedback.\n\nThe same applies to agents.\n\nFeedback helps the agent understand whether the task is completed or whether more iterations are required.\n\nFor example, a coding agent might:\n\nThe feedback helps decide what happens next in the loop.\n\nAs developers, we stop working on a task once the feedback shows that everything is correct.\n\nThe same stopping mechanism is required for agents.\n\nWithout stop conditions, an agent can keep performing actions without knowing when the task is actually complete.\n\nExamples:\n\nA good loop knows when to continue and when to stop.\n\nThere are also other important parts of production-level agent loops, such as memory, state management, tool permissions, recovery strategies, and verification systems.\n\nThe exact design depends on the type of task the agent is solving.\n\n| Prompt Engineering | Loop Engineering |\n|---|---|\n| Focuses on improving instructions | Focuses on improving the entire workflow |\n| One prompt gives one response | A goal creates multiple iterations |\n| Human provides continuous feedback | Agent uses feedback from the environment |\n| Controls what the AI says | Controls how the AI works |\n\nSuppose we are creating a coding agent.\n\nIf we simply tell it:\n\n```\nFix this bug\n```\n\nIt will look at the code, try to fix it, and probably say that the task is complete.\n\nBut what do we do as developers?\n\nWe don't just write code and stop.\n\nWe verify it.\n\nWe run tests.\n\nWe check if the issue is actually fixed.\n\nWe look for side effects.\n\nSo just prompting an agent is not enough.\n\nWe need to define the loop.\n\nA normal prompt:\n\n```\nFix this bug\n```\n\nA loop-based approach:\n\n```\nFind the bug\n ↓\nUnderstand the code\n ↓\nCreate a fix\n ↓\nRun tests\n ↓\nAnalyze failures\n ↓\nImprove solution\n ↓\nRepeat until tests pass\n```\n\nThe difference is not just the prompt.\n\nThe difference is the process around the AI.\n\nAs we see this AI space constantly evolving, we have only recently started seeing terms like Loop Engineering become popular.\n\nBut the important thing is understanding where the industry is moving.\n\nAI is moving from simple chat-based interactions to systems that can take actions and complete tasks with less human intervention.\n\nThe next challenge is not only creating smarter models.\n\nIt is creating better systems around those models, where prompts, tools, memory, verification, and feedback loops work together.\n\nAnd that is where concepts like Loop Engineering become important.\n\nAI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.\n\n[git-lrc](https://github.com/HexmosTech/git-lrc) fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.\n\nAny feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.\n\nGive it a ⭐ [star on Github](https://github.com/HexmosTech/git-lrc)", "url": "https://wpnews.pro/news/ai-agents-are-not-magic-they-are-just-good-feedback-loops", "canonical_source": "https://dev.to/rijultp/ai-agents-are-not-magic-they-are-just-good-feedback-loops-lhm", "published_at": "2026-07-28 18:35:54+00:00", "updated_at": "2026-07-28 19:04:20.573210+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "developer-tools", "machine-learning", "large-language-models"], "entities": ["Rijul", "git-lrc", "GPT"], "alternates": {"html": "https://wpnews.pro/news/ai-agents-are-not-magic-they-are-just-good-feedback-loops", "markdown": "https://wpnews.pro/news/ai-agents-are-not-magic-they-are-just-good-feedback-loops.md", "text": "https://wpnews.pro/news/ai-agents-are-not-magic-they-are-just-good-feedback-loops.txt", "jsonld": "https://wpnews.pro/news/ai-agents-are-not-magic-they-are-just-good-feedback-loops.jsonld"}}