What is loop engineering? The AI trend replacing prompt engineering

In recent months, Artificial Intelligence (AI) usage has changed drastically, from back-and-forth conversation, automation, to AI agents. The idea of using generative AI used to be quite simple, you write a prompt, get a response, and use it as you desire. However, this process is not enough to meet the growing demand for AI workflows that include multi-step actions, decision-making, and self-correction. 

Therefore, to meet the growing demands, AI systems need to work in loops, and this is where “loop engineering” comes in. Therefore, companies have started to replace the traditional prompt-and-response process and opt for the loop process to meet their desired goal.

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What is loop engineering?

Engineers have reportedly stopped writing prompts for tools like Anthropic's Claude Code or OpenAI's Codex; they have started writing AI loops that let AI systems run continuously. This process does not require back-and-forth interaction or a human to intervene at each step. 

Therefore, in this case, AI doesn’t just respond; it acts, evaluates its action, decides what to do next, and repeats the cycle. But where does it start? Well, you will have to give AI a goal and a feedback mechanism for a single instruction to work effectively. 

Claude Code creator Boris Cherny recently said, “I don't write the prompt anymore. Claude writes the prompt, and now I'm talking to that new Claude that is kind of coordinating.”

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OpenAI’s Peter Steinberger, also the man behind Openclaw, said, “You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents.”

What does a loop look like in practice?

The AI loop begins by giving the AI a specific goal, and not just a prompt. Based on the goal, it will start to conduct necessary actions like writing code, searching the web, or drafting a document. Then it evaluates its own output based on the desired goal to determine whether it meets the required criteria. 

If the outputs are not suitable, the AI adjusts its approach and tries again, and the correction works in a loop until the output meets the set criteria. Many engineers and companies have started to adopt loop-based workflows.

How are AI loops different from prompts?

Prompt majorly relies on a single instruction, which is given to the AI. Therefore, it relies heavily on using the right words, the right context, and the right format to get the desired response. This also requires the user to manually refine prompts if the output isn't satisfactory.

For the AI loop, the responsibility is shifted to the AI system, as it does not require a human to refine the prompt; the system itself refines its process. In simpler terms, Loop engineering makes AI systems more reliable and hassle-free for business use.