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# The New AI prompting Method Everyone Is Talking About: Loops (And How To Use Them)

**[YouTube](https://daily.dev/sources/youtube)** · 19 min read · 1 upvotes · 0 comments

## Summary

Loop engineering is an emerging AI workflow pattern where agents prompt themselves iteratively rather than requiring constant human input. The approach involves an orchestrator agent delegating tasks to specialist sub-agents (builder, scout, growth), a verification step to check goal completion, and persistent memory to track progress across cycles. Two variants exist: open loops (unconstrained, token-heavy) and closed loops (bounded goals, cost-controlled). A practical demo using Claude Code shows three parallel agents building a pickleball e-commerce marketing system — a Harry Potter personality quiz, a content research engine, and a growth/marketing agent — all looping on a weekly schedule. Additional use cases cover freelancers, students, shop owners, and content creators.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=Ry3YyG22EUc>

## Questions this post answers

### What is the difference between an open loop and a closed loop when running AI coding agents?

An open loop tells an agent to explore broadly and decide what to do on its own, which can surface ideas a human wouldn't think of but burns very large amounts of tokens since the agent keeps going in new directions indefinitely. A closed loop starts from a bounded, human-set goal with a defined path and clear evaluation at each step, keeping token spend far more predictable and constrained.

_daily.dev helps developers weigh agent workflow trade-offs like open versus closed loops before committing budget._

### What is loop engineering in AI agent workflows?

Loop engineering is a workflow where a human sets a goal once, then AI agents handle discovery, planning, parallel execution, and verification themselves, iterating until a verification step confirms the goal is met, rather than the human manually re-prompting after every response. Memory files stored outside the conversation track completed steps so agents can pick up where a previous cycle left off. Boris Cherney (Claude Code) and Peter Steinberger (OpenClaw) are cited as proponents of this approach.

_developers building agent-driven pipelines can follow evolving practices like loop engineering on daily.dev._

## Similar posts on daily.dev

- [You’re Not Prompting Anymore. You’re Engineering Loops.](https://daily.dev/posts/you-re-not-prompting-anymore-you-re-engineering-loops--aknavecvs) · Medium · 1 upvotes · 0 comments
- [Loop Engineering](https://daily.dev/posts/loop-engineering-h663rbtdz) · Elevate · 3 upvotes · 0 comments
- [Stop Prompting. Design the Loop.](https://daily.dev/posts/stop-prompting-design-the-loop--zomwvtp2n) · Pulumi · 3 upvotes · 0 comments
- [An Introduction to Loop Engineering](https://daily.dev/posts/an-introduction-to-loop-engineering-7ctzwxfxz) · Machine Learning Mastery · 2 upvotes · 0 comments

---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#automation](https://daily.dev/tags/automation), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#claude-code](https://daily.dev/tags/claude-code)

[View this post on daily.dev](https://daily.dev/posts/the-new-ai-prompting-method-everyone-is-talking-about-loops-and-how-to-use-them--x3n96dhfr)

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