Based on a YouTube video
Agent Loops

Finally,
clearly
explained.

Watch the original: youtu.be/EuzYhzB0vbI

The Starting Point

You're already in a loop.
You just run it by hand.

Every time you prompt an AI, read the output, tweak your approach, and try again — that's a loop. It's working, it's just slow. Because you're doing the iteration.

💬
You prompt
Give the AI a task. Wait for a response.
👀
You review
Read the result. Spot what's off. Figure out how to improve it.
🔁
You iterate
Refine the prompt. Try again. Repeat until it's good enough.
“
Loop engineering is replacing yourself as the person who prompts the agent.
— The core idea behind agent loops
The Core Concept

An agent that iterates
without you.

An agent loop reasons, acts, checks its own work, and repeats — until it hits a defined goal. No human in the middle.

🧠
Reason
Plan the next move
→
⚡
Act
Execute the action
→
🔍
Observe
Check the result
→
🔁
Repeat
Until goal is met
Anatomy

Every loop has
three parts.

01 — Trigger
🎯
What starts it?
A command, a schedule, an event. The signal that kicks things off.
02 — Action
🛠
What does it do?
Generate, evaluate, edit, build, compare, test. Whatever moves toward the goal.
03 — Stop Condition
✅
When does it end?
The most critical part. Without a clear stop condition, your loop runs forever — burning time and money.
Foundations

Two things every
good loop needs.

Pillar 01
🎯
A clear,
objective goal
Vague goals produce vague loops. "Make it better" doesn't work. "Score above 8/10 on these criteria" does. The more specific and measurable, the better the agent can self-direct.
Pillar 02
🔬
A verification
mechanism
The agent needs a way to check its own work. Scoring rubrics, tests, screenshots, linters — these are all verification. Without it, the agent can't know if it's improving, or when to stop.
The Analogy

Think of it like
a smart intern.

1
Plans the next move — figures out what to do without waiting to be told each step.
2
Does the work — executes using whatever tools are available.
3
Reviews their own output — checks the result against the goal before moving on.
4
Repeats until done — keeps iterating until the standard is met.
"A smart intern you don't micromanage."
You set the brief. You define what "good" looks like. The agent figures out how to get there — and keeps going until it does.
Why It Works

First attempt ≠
final answer.

Low High v1 v2 v3 v4 v5+ Human Agent Iterations → Quality →
Every time you give feedback and iterate, quality goes up. Each cycle narrows the gap.
The loop outsources that iteration to the agent. Instead of you doing 5 rounds by hand, the agent runs them autonomously — then hands you the polished result.
The agent doesn't stop at good enough. It stops when you defined good enough.
In the Wild

Loops that actually ran.

🎨
Thumbnail Design
Agent generated 10 concepts, scored each against a rubric, then iterated on the top 3. Reached version 7 entirely autonomously.
✓ Done in 27 minutes
🌐
3D Visualisation
Loop built a Three.js plane, rendered it in a real browser, verified visually, and iterated across multiple versions until the output looked right.
✓ Browser-verified output
🖼
HTML Art Recreation
Agent recreated Abbey Road imagery in pure HTML/CSS. Took a screenshot after each iteration to verify progress. 7 versions total.
✓ 7 verified iterations
Reality Check

You don't need
24/7 loops.

❌ The Myth
You need fleets of agents running around the clock
Most advice assumes you're a hardcore developer spinning up dozens of parallel agents at all hours. That's not most people — and it's not necessary.
✓ The Reality
30 minutes to a few hours is the real-world sweet spot
Scheduled or event-triggered loops running for a short burst get most of the benefit. You don't need continuous operation for the majority of real workflows.

Customise based on your actual workflow — not what scale-obsessed experts are doing.

Make It Work

What separates a good
loop from a bad one.

A clear, checkable goal — not vague direction
A verification mechanism the agent can run itself
The right tools for the job (browser, code runner, etc.)
A defined stop condition — when is "done" actually done?
A max iteration limit — cost protection, not optional
A trigger that fits your actual workflow — not someone else's
That's the loop.
Watch the full 20-minute video — it's well worth it.
youtu.be/EuzYhzB0vbI