What Is Loop Engineering? Building Better AI Agents
Clip title: What Is Loop Engineering? Building Better AI Agents Author / channel: IBM Technology URL: https://www.youtube.com/watch?v=DQBqh6gcx7U
Summary
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Loop engineering, a new evolution of prompt engineering, designs systems that perform prompting tasks for the user. It involves creating a loop consisting of a goal, action, observation, and adjustment. The goal should be specific with an end condition. A schedule or hook can trigger the loop.
Context engineering involves choosing what passes the loop can see, such as through compaction or the progressive disclosure of agent skills. A spine stores persistent state, and agents use tools to execute tasks. Subagents act as checkers and co-workers.
While loop engineering requires minimal human involvement, humans are still needed to write goals and intent, and read output. The four pitfalls of loop engineering are unverified code, comprehension debt, intent debt, and cognitive surrender. Loop engineering works best with a human in the loop.
Video Description & Links
Description
Learn more about Loop Engineering here → https://ibm.biz/~LqlzZo42O
AI agents need more than one perfectly written prompt. Martin Keen explains loop engineering and how AI agents act, observe, adjust, and repeat toward measurable goals. Learn how context, tools, subagents, and human oversight make agentic loops work.
00:00 – Understand Loop Engineering vs Prompt Engineering 01:18 – Build an AI Agent Loop Around a Goal 02:30 – Define Goals AI Agents Can Actually Verify 03:29 – Engineer Triggers, Schedules & Hooks 04:18 – Manage Agent Context, Skills & Persistent State 05:37 – Give AI Agents Tools with MCP & APIs 06:10 – Use Subagents to Check AI Agent Work 07:11 – Keep Humans Inside Agentic Loops 07:49 – Avoid Comprehension & Intent Debt 10:05 – Define the Human Role in Loop Engineering
AI was used in the creation of the transcript and metadata for this video.
loopengineering aiagents agenticai promptengineering contextengineering
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