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Harness • Loop • Graph Engineering - Five Stages of AI Agents Beyond Prompts

AI agents can feel vague, right? We’ll share practical problem-solving know-how from real-world experience, covering everything from prompts and loops to graph engineering systems.

(5.0) 11 reviews

137 learners

Level Basic

Course period Unlimited

multi-agent
multi-agent
context-switching
context-switching
AutoGPT
AutoGPT
AI Agent
AI Agent
multi-agent
multi-agent
context-switching
context-switching
AutoGPT
AutoGPT
AI Agent
AI Agent

[Update] Notice regarding the addition of Harness practice lectures

Hello, students.

I have newly added the following two lectures.

<Section 3>

  • 5. Summary of Harness Concepts for Practice (20:53)

  • 6. Working through the Portfolio Creation/Review Harness System step-by-step (21:14)

This is a hands-on exercise where you will actually build the CLAUDE.md, Skills, Sub-agents, Hooks, and MCP you have learned so far, step-by-step, into a single project ("AI Portfolio Creation/Review Harness System").

In Lecture 5, we summarize why they must be stacked in this specific order and what falls apart if one element is missing. In Lecture 6, we have organized a step-by-step hands-on session where you can follow along by opening practice files ranging from step0 (base state) to step5 (completed state).

I have prepared this as a final practice session to tie all the concepts together and apply them to real-world tasks, so please use it as a reference.

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