Cursor AI大师课：从代码补全到智能体工程全流程实战_CG群

《The AI Orchestrator》Cursor AI大师课共193节超31小时，教你用证据驱动工作流构建智能体自动化工程系统，涵盖.cursorrules、MCP、多智能体协作与CI/CD，适合追求企业级AI开发流的工程师。

原始标题：Cursor AI Masterclass: Engineering Workflows & Methods

Cursor AI Masterclass: Engineering Workflows & Methods

这门名为《The AI Orchestrator》的全栈 AI 软件工程进阶实战课程发布于 2026 年 9 月 [2026]，共包含 193 节课（超 31 小时）[2026]。它核心聚焦于将 Cursor 从简单的代码补全工具升华为工业级的 AI 智能体（Agents）自动化工程系统，教导开发者如何通过严谨的证据驱动工作流，进行软件的规划、编码、调试、测试、评审与上线发布。

课程不仅涵盖 Cursor 的高级特性（如 .cursorrules、AGENTS.md、MCP 协议、多智能体协作与云端 Agent）[2026]，还深度融合了 GitHub CI/CD、大模型应用开发（RAG、智能体状态图、Prompt 防注入防御）以及量化可观测性（耗时、成本与质量评估） [2026]。它专为拒绝碎片化 Prompt、追求可复用且安全的企业级 AI 辅助开发流的工程师与技术负责人打造。

Build, debug, test, review, and ship software with Cursor agents, GitHub, cloud workflows, RAG, and AI automation.

What you’ll learn
⚡ Use Cursor AI to plan, implement, debug, test, review, and ship production-ready software through an evidence-driven workflow.
⚡ Design reliable agent workflows with clear context, permissions, tool contracts, checkpoints, and human approval boundaries.
⚡ Apply Git, GitHub, CI, worktrees, cloud agents, MCP, automation, and observability to realistic engineering projects.
⚡ Build and evaluate AI product features including RAG, retrieval, agent state graphs, security tests, quality, latency, and cost.

Requirements
❗ Basic programming experience and familiarity with a code editor, terminal, and Git are helpful, but advanced AI experience is not required.
❗ You need a computer that can run Cursor, access to a practice repository, and willingness to execute commands and inspect results.

Description
This course contains the use of artificial intelligence.

Turn Cursor from a code-completion tool into a disciplined, production-ready engineering system. This comprehensive hands-on course teaches you how to use AI across the full software delivery lifecycle: understanding requirements, planning architecture, implementing changes, debugging with evidence, testing, reviewing, releasing, and learning from real outcomes.

You will build a professional engineering workflow around Cursor, Git, GitHub, agents, cloud environments, automation, MCP tools, and repeatable verification. The course begins with the foundations of context, models, tools, runtime boundaries, and ownership. It then moves into repository navigation, rules and AGENTS md, task planning, architecture decisions, safe migrations, worktrees, parallel development, subagents, and reliable handoffs.

The projects go beyond toy prompts. You will work through realistic application scenarios involving APIs, databases, frontend flows, authorization, CI, pull requests, observability, incidents, and release recovery. You will also build and evaluate AI-powered product capabilities: ingestion pipelines, retrieval and RAG, source-grounded answers, runtime agent graphs, tool contracts, prompt-injection defenses, quality evaluation, latency, and cost measurement.

Every lesson is designed around observable evidence. You will learn to define acceptance conditions, inspect diffs, preserve existing behavior, choose the right Cursor mode, collect logs and test results, review agent output, and stop automation safely when the evidence is insufficient. Dedicated modules cover Cursor CLI and SDK workflows, Cloud Agents, self-hosted machines, team operations, GitHub review automation, and long-running engineering work.

By the end, you will have a reusable professional workflow for shipping software with AI while keeping human judgment, security, maintainability, and verification at the center.

Who this course is for
⭐ Software developers and technical leads who want to use Cursor and AI agents professionally, from fundamentals through advanced team workflows.
⭐ Engineers moving from ad-hoc prompting to repeatable, reviewable, and secure AI-assisted software delivery.
⭐ Developers building AI-powered products with APIs, data pipelines, retrieval, RAG, evaluations, and runtime agent workflows.

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