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Finally. Agent Loops Clearly Explained.

Nate Herk | AI Automation19 June 2026Watch on YouTube

Part of series

Ep. 4 · Loop Engineering

Loop engineering als de nieuwe standaard voor autonome AI-agenten die iteratief taken uitvoeren zonder menselijke tussenkomst.

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Description

My FREE AI OS Course: https://www.skool.com/ai-automation-society/about?el=agent-loops-explained&hcategory=youtube-videos&utm_campaign=free-group Full courses + unlimited support: https://www.skool.com/ai-automation-society-plus/about?el=agent-loops-explained&hcategory=youtube-videos&utm_campaign=ais-plus Apply for my YT podcast: https://podcast.nateherk.com/apply Work with me: https://uppitai.com/ My Tools💻 FREE MONTH voice to text: https://get.glaido.com/nate Code NATEHERK for 10% off VPS (annual plan): https://www.hostinger.com/vps/claude-code-hosting Everyone is talking about agent loops and loop engineering right now, but most of the advice assumes you are a hardcore coder running fleets of agents around the clock. In this video I break down what an agent loop actually is (reason, act, observe, repeat), why the verification step matters more than the architecture, and how to think about a "done" criteria that your agent can actually check. I walk through a few real loops I ran, including thumbnail scoring, a three.js plane, and an Abbey Road recreation, and explain why loops are about getting you closer on the first try, not perfect output. If you have been feeling behind because you are not running five agents at once, this one is for you. Sponsorship Inquiries: 📧 nate@smoothmedia.co Connect with me: https://www.linkedin.com/in/nateherkelman/ https://x.com/nateherk https://www.instagram.com/nateherk/ TIMESTAMPS 0:00 Intro 0:31 What Loop Engineering Means 2:23 How an Agent Loop Works 5:29 Three Ways to Build Loops 6:13 Demo: Thumbnail Scoring 8:12 Demo: Three.js Plane 9:08 Demo: Abbey Road Recreation 10:42 What Makes a Loop Work 12:40 Does This Apply to You? 14:06 Resources & Wrap-Up

What you'll learn

  • Agent loops follow a reason-act-observe-repeat cycle, where an AI system reasons, acts, observes, and repeats the process until the goal is achieved.
  • The verification step is more critical than the system architecture, as it determines whether the result meets requirements.
  • A clear 'done' criteria is essential so the agent can determine when the loop is complete.
  • Loop engineering is about getting closer to the right result on the first try, not about generating perfect output.
  • Agent loops are accessible to everyone, not just developers running multiple agents simultaneously.

Frequently asked questions

What is the basic cycle of an agent loop?
The basic cycle consists of four steps: reason, act, observe, and repeat. The agent analyzes the situation, takes action, checks the result, and repeats this process until the goal is achieved.
Why is the verification step so important in agent loops?
The verification step determines whether the result meets requirements and whether the loop can end. This is more important than system architecture because it decides whether the work is actually completed.
What does 'done' criteria mean and why is it needed?
The 'done' criteria is a clear definition of when the agent should stop executing the loop. It is needed so the agent can determine when the goal is reached without manual intervention.
Who are agent loops suitable for?
Agent loops are suitable for everyone, not just developers running multiple agents simultaneously. They are a practical way to make AI systems work more effectively.

Topics

agent loopsai automationloop engineeringagentic aitutorial