Descriptions:
Stop prompting AI agents one step at a time. In this video, I build a real agent loop that takes a goal, executes the work, verifies its own output, and only comes back when it is done, blocked, or needs approval.
The practical demo turns a Notion status change into a complete YouTube editing workflow using Claude Code and an existing editing skill. I break down the triggers, execution skills, verifiers, memory, training mode, approval gates, and stop conditions that make autonomous agents genuinely useful without giving them reckless control.
The same loop engineering framework can be applied to Claude Code, Codex, Cursor, Hermes Agent, and almost any AI agent.
What’s covered:
• When a task deserves a loop
• The four parts of loop engineering
• How to turn a proven workflow into an agent loop
• Building a Notion-triggered YouTube editing loop
• Training the loop before increasing autonomy
• Machine verifiers versus human judgment
• Where Claude Code and Hermes Agent fit
• The loops you should never build
• A reusable framework for building your first loop
0:00 Stop prompting, start building loops
0:53 The prompting trap
1:37 When a task deserves a loop
3:03 The four parts of loop engineering
5:22 Start with a proven workflow
7:01 Building a real YouTube editing loop
7:51 The loop prompt
11:04 The first AI-generated edit
13:13 Machine verifiers vs human judgment
14:33 Why training mode matters
16:01 Claude Code vs Hermes Agent
16:30 Loops you should never build
17:43 The reusable loop template
19:05 Prompting vs loop engineering
Comment “LOOP” if you want more practical AI agent systems like this.
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