You Can Learn Loop Engineering, Graph Engineering in 18min: Production AI Agent, Harness Engineering

Описание: Loop Engineering, Graph Engineering, and Harness Engineering explained through a real Production AI Agent system. In 18 minutes, I use a five-agent insurance claims workflow to show how prompts, context, execution, loops, graphs, evals, and production controls work together.

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You will learn:

• Why Prompt Engineering and Context Engineering are not enough
• How Agent Execution Engineering controls how one agent acts
• How Loop Engineering adds evaluation, feedback, retries, stop rules, and human gates
• How Graph Engineering coordinates agents and connects knowledge through GraphRAG and Graph Memory
• How Harness Engineering adds validation, permissions, observability, evals, audit trails, and human review
• How all these layers become one usable Production AI Agent system

The example uses a five-agent insurance claims team:

• Intake Agent
• Damage Evidence Agent
• Policy Agent
• Risk Agent
• Payout Agent

The workflow is orchestrated with LangGraph and includes conditional routing, evaluation loops, human review, production thresholds, and 72 evals.

About Me:
I'm Maryam, PhD, built 400+ Production AI Agents, with 20+ years experience in AI, building and teaching production agents across 130+ countries.

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   / @maryammiradi  

CHAPTERS

00:00 Loop Engineering, Graph Engineering and Harness Engineering
00:32 The Insurance Claims Agent Problem
01:13 Prompt Engineering
01:50 Context Engineering
03:06 Agent Execution Engineering
04:48 What Is Loop Engineering?
05:21 Adding Loops to a LangGraph Workflow
06:50 Four Levels of Agent Loops
08:02 Design Patterns vs Loop Engineering
08:52 Andrew Ng’s Three Product Loops
10:53 Why Loop Engineering Is Everywhere
11:17 Graph Engineering and Orchestration Engineering
12:10 GraphRAG, Graph Memory and Hybrid Retrieval
14:30 Harness Engineering and AI Agent Evals
17:08 Production Agent Engineering
18:00 Build the Complete Insurance Claims Agent Team

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