Today, AI applications have moved beyond simple conversational entertainment and fully entered the era of enterprise-grade engineering implementation. The vast majority of developers, AI practitioners, and enterprise technical staff have obvious shortcomings: they can only call ready-made models, do not understand AI engineering iteration paradigms, and cannot build stable, controllable, and iterable agent architectures; the AI Agents they build have no memory, no constraints, and chaotic execution, making them highly prone to logical loopholes, uncontrolled output, task failure, and other problems; they lack standardized development specifications, and project development is fragmented and messy, unable to meet formal enterprise launch standards; they cannot build structured automated workflows, so AI can only provide simple assistance and cannot handle complex business scenarios; they want to implement commercial AI projects but do not understand RAG retrieval mechanisms, multi-model collaboration, or project debugging and deployment, ultimately remaining stuck at the demo stage and unable to achieve commercial implementation.

This Harness Engineering enterprise-level application practical course focuses on the industry's cutting-edge AI engineering system, from underlying paradigms, core principles, and architectural logic to major-company cases, standardized specifications, and enterprise-level project practice, creating a complete commercially usable AI engineering implementation system to help learners advance from "knowing how to use AI" to professional engineering talent who "can build, develop, and implement enterprise-level AI systems." The course starts with basic Harness concepts and engineering paradigm shifts, deeply analyzing the underlying principles of AI engineering, including core mechanisms such as context engineering, persistent memory, structured execution, architectural constraints, and feedback loops, and combines practical cases from major companies such as OpenAI, Anthropic, and Stripe to help learners understand how truly enterprise-level AI systems are designed and iterated.

In the practical segment, the course will guide learners to apply the SDD specification-driven development method to build a complete enterprise-level RAG intelligent customer service system from scratch. Learners will personally complete the entire process of project requirement breakdown, architecture design, environment setup, large model integration, knowledge base management, dialogue page development, and project debugging and troubleshooting. Through this complete learning path, learners will not only master the core methodology of AI engineering implementation, but also accumulate commercially viable AI project experience that can be directly showcased, truly gaining the ability to independently undertake enterprise AI development projects. This course is a key course to help technical developers break through AI application bottlenecks and achieve the leap from tool users to system builders.

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• 1.Harness Course Introduction
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• 2.Harness Basic Concepts
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• 3.Three Leaps in AI Engineering Paradigms
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• 4.Harness Underlying Principles and Application Guide
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• 5.Harness Practical Value
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• 6.What Problems Does Harness Solve
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• 7.Harness Context Engineering
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• 8.Harness Agent Role Division
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• 9.Harness Persistent Memory
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• 10.Harness Structured Execution
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• 11.Harness Architectural Constraints
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• 12.Harness Feedback Loops
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• 13.Harness Entropy Management
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• 14.Harness Industry Best Practice Cases
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• 15.OpenAI Harness Engineering Practice Case
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• 16.Anthropic Harness Engineering Practice Case
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• 17.Stripe Harness Engineering Practice Case
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• 18.Huntley Harness Engineering Practice Case
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• 19.Six Industry Implementation Guidelines for Harness
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• 20.Harness Practical Development of Enterprise-Level Projects
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• 21.Harness Stage 1 Information Layer - Let Agents Understand the Project
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• 22.Harness Stage 2 Constraint Layer - Prevent Agents from Making Mistakes
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• 23.Harness Stage 3 Automation Layer - Let Agents Self-Verify and Repair
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• 24.Harness Practical: Building a New Project from Scratch
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• 25.Common Issues in Harness Engineering Practice
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• 26.Harness Engineering Implementation Checklist
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• 27.Harness Practical: Developing Project Features and Test Verification
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• 28.SDD Specification-Driven Development
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• 29.SDD Core Concepts and In-Depth Implementation
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• 30.SDD Project Practical Application
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• 31.SDD Multi-AI Collaboration
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• 32.Recommended Harness Open-Source Tools
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• 33.Harness Engineering Summary
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• 34.Codex Develops RAG Intelligent Customer Service System Based on Harness
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• 35.RAG Core Workflow and Vector Database Retrieval Mechanism
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• 36.Codex Project Development Environment Preparation
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• 37.Codex Uses CC Switch to Switch Domestic Large Models
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• 38.Codex Project Requirement Breakdown and Architecture Design
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• 39.Codex Project Initialization and Dependency Management
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• 40.Codex Develops Large Model Integration Layer Business Features
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• 41.Codex Develops RAG Retrieval Tools
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• 42.Codex Develops Knowledge Base Management Page
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• 43.Codex Develops Customer Service Chat Page and Agent Workflow
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• 44.Codex Develops System Entry and Multi-Page Navigation
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• 45.Codex Project Running, Debugging, and Troubleshooting
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• 46.Codex Intelligent Customer Service Project Review and Summary
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💡 Learning Tip:Content reflects industry practices at time of publication. Please refer to the latest platform rules when learning.