AI Agents Are Not Magic. They Are Just Good Feedback Loops

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.

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 Past few years, when things like GPT came out, we were mainly learning how to use them. Which basically meant using better prompts. So we were continuously refining prompts and trying to get better outputs. And that is fine. This was basically an exchange back and forth between us and the bot. We ask something. The AI responds. We refine our prompt. The AI responds again. This loop continues. But now we are seeing the rise of AI agents. We see them everywhere, in every LinkedIn post, every YouTube video. The 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. Here, agents are expected to work in loops. They do one thing. Then they review the result, identify additional things that need to be done, take action again, and repeat. The main input we give is usually a goal or a prompt. But this alone will not give us better control over how the agent works. Reliable 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. The agent works in loops. So we need a better way to design and control these loops. Agents perform tasks through loops. So instead of only focusing on prompt engineering, we also need to engineer the loop in which the agent operates. This is where Loop Engineering comes in. The term has recently gained attention as people started exploring better ways to build reliable workflows around AI agents. The idea is simple: Instead of only telling an agent what to do, we design the process that helps the agent complete the task reliably. Prompting is still an important part of this process. But it becomes one layer in a bigger system that includes actions, feedback, memory, verification, and stopping conditions. There are many ways to think about designing agent loops. A simple way to understand them is through three important parts: Actions are anything that an agent needs to perform. Take us humans as an example. When we build software, we write code, read code, interact with files, create new files, run commands, and test our changes. These are actions. Similarly, for an agent, actions are the things it can perform using its available tools. Examples: Without actions, an agent has limited ability to interact with the world and becomes closer to a traditional chatbot. Just performing actions is not enough. As humans, we don't just complete a task and assume it is correct. We verify our work and look for feedback. The same applies to agents. Feedback helps the agent understand whether the task is completed or whether more iterations are required. For example, a coding agent might: The feedback helps decide what happens next in the loop. As developers, we stop working on a task once the feedback shows that everything is correct. The same stopping mechanism is required for agents. Without stop conditions, an agent can keep performing actions without knowing when the task is actually complete. Examples: A good loop knows when to continue and when to stop. There are also other important parts of production-level agent loops, such as memory, state management, tool permissions, recovery strategies, and verification systems. The exact design depends on the type of task the agent is solving. | Prompt Engineering | Loop Engineering | |---|---| | Focuses on improving instructions | Focuses on improving the entire workflow | | One prompt gives one response | A goal creates multiple iterations | | Human provides continuous feedback | Agent uses feedback from the environment | | Controls what the AI says | Controls how the AI works | Suppose we are creating a coding agent. If we simply tell it: Fix this bug It will look at the code, try to fix it, and probably say that the task is complete. But what do we do as developers? We don't just write code and stop. We verify it. We run tests. We check if the issue is actually fixed. We look for side effects. So just prompting an agent is not enough. We need to define the loop. A normal prompt: Fix this bug A loop-based approach: Find the bug ↓ Understand the code ↓ Create a fix ↓ Run tests ↓ Analyze failures ↓ Improve solution ↓ Repeat until tests pass The difference is not just the prompt. The difference is the process around the AI. As we see this AI space constantly evolving, we have only recently started seeing terms like Loop Engineering become popular. But the important thing is understanding where the industry is moving. AI is moving from simple chat-based interactions to systems that can take actions and complete tasks with less human intervention. The next challenge is not only creating smarter models. It is creating better systems around those models, where prompts, tools, memory, verification, and feedback loops work together. And that is where concepts like Loop Engineering become important. AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production. 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. Any feedback or contributors are welcome It's online, source-available, and ready for anyone to use. Give it a ⭐ star on Github https://github.com/HexmosTech/git-lrc