109 lines
5.5 KiB
Markdown
109 lines
5.5 KiB
Markdown
---
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title: "AI 开发范式演进：从 Prompt Engineering 到 Loop Engineering (Raw)"
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source: https://mp.weixin.qq.com/s/hcgKahtQRE2QqI6xplv2Rg
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author: 邱汉宸（东南大学、阿里淘天）
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platform: Datawhale
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date: 2026-06-29
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---
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# AI 开发范式演进：从 Prompt Engineering 到 Loop Engineering
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## 引言
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2023 年是大语言模型落地应用的早期阶段，"年薪百万的提示词工程师"刷屏。工业界核心精力投射于提示词工程，方法论侧经历系统化演进（Zero-shot → Few-shot → Chain-of-Thought → Tree-of-Thought）。
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转折在 2025–2026 年，三句话引爆 AI 社区：
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1. "I really like the term 'context engineering' over prompt engineering." — Tobi Lütke
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2. "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." — Peter Steinberger
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3. "I don't prompt Claude anymore. I have loops running. My job is to write loops." — Boris Cherny
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核心命题：人类从 Agent 循环的内部走向外部，从执行者变成设计者。
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## 四次浪潮
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### 1. Prompt Engineering
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- 方法论：Zero-shot/Few-shot, Instruction Prompting, APE 自动 Prompt 搜索
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- Prompt Engineering ≠ Blind Prompting（trial-and-error 无测试）
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- 声明式框架：DSPy, APE — 开发者声明输入输出签名，优化器自动搜索最优 Prompt
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- 瓶颈：上下文窗口限制、缺乏记忆与工具调用、维护成百上千条模板的技术债务
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### 2. Context Engineering
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- 三套方法论：MVC（Minimum Viable Context）、GraphRAG、Just-in-Time 检索
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- 三种故障模式：Context Starvation / Context Overflow / Context Rot
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- 隐式维度：提示词缓存（Prompt Caching）+ 前缀匹配不变性（Prefix Matching Invariant）
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- "从静到动"分层排列：工具定义 → 系统提示 → 历史对话 → 动态消息
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- 缓存经济学：N>3 即可净收益（首次 100%，后续 20%）
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- Anthropic Skills 采用 Just-in-Time 设计哲学
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### 3. Harness Engineering
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- 公式：Agent = Model + Harness
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- 四大支柱：环境资产与工具集 / 控制与编排逻辑 / 规则中间件（Hooks）/ 运行时可观测性
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- 信任边界：物理基础设施 → 安全沙箱 → Agent Harness → 运行时 → 模型
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- DataTalks.Club 事故：Claude Code 执行 terraform destroy 抹除生产数据库
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- 八条非妥协原则：
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1. Model proposes — Harness executes
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2. Every call returns a result
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3. Risk changes the process
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4. Draft 与 Commit 分离
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5. Context is assembled, not dumped
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6. Long tasks have budgets
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7. Skills & Connectors 渐进式披露
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8. Recurring failures become Harness features
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- CodeRabbit 分层拦截流水线：确定性规则层 → 策略网关层 → AI 审查层 → 人类终审
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- Skill Issue 框架：Agent 表现不佳 → 排查 Harness 代码
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- Terminal Bench 2.0：不改模型，仅改写 Harness → 排名 30 → 前五
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### 4. Loop Engineering
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- 公式：Loop = Cron + 决策器
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- 哲学：机制（Mechanism）与策略（Policy）分离
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- 三级成熟度：Open Loop → Closed Loop → Review Loop
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- 五件套 + 一个记忆：Automations / Worktrees / Skills / Connectors (MCP) / Sub-agents / State 文件
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- Loop Contract 六维约束：TRIGGER / SCOPE / ACTION / BUDGET / STOP / REPORT
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- 安全机制：熔断器（Circuit Breaker）+ 看门狗（Watchdog）
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- 自主闭环流水线：AI 编码 → 沙箱测试 → 日志回灌 → AI 修复 → CI 绿标 → 自动发起 PR
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## 嵌套关系
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Prompt ⊂ Context ⊂ Harness ⊂ Loop
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## 早期 vs 当前
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- 早期：Output = f(Prompt, Context) — 可靠性取决于输入质量
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- 当前：Success = g(Loop(State, Harness, Model)) — 取决于循环深度和验证器严密性
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## Loop Engineering 的影响
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1. 为缓解幻觉提供可工程化的收敛路径（Text → Code → Execute → Read Result → Self-correct）
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2. 自动化控制范式升级（容错、自愈、动态自适应）
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3. 基础设施产品原语化（HaaS）
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## Loop Designer 角色
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1. 定义终止边界（Goal & Verifier 设计）
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2. 维护工具链与领域资产（Tooling & Skill 配置）
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3. 设计安全断路器（Human-in-the-Loop & Budget Guard）
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## 参考资料
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[1] Lilian Weng. Prompt Engineering. 2023.
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[2] Mitchell Hashimoto. Prompt Engineering vs. Blind Prompting. 2023.
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[3] Lilian Weng. LLM Powered Autonomous Agents. 2023.
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[4] Tobi Lütke. Context engineering over prompt engineering. 2025.
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[5] Michael Hunger. Why AI teams are moving from prompt engineering to context engineering. 2026.
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[6] Tomás Murúa. Context engineering vs. prompt engineering. 2026.
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[7] Vivek Trivedy. The Anatomy of an Agent Harness. 2026.
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[8] Sergio Paniego & Aritra Roy Gosthipaty. Harness, Scaffold, and the AI Agent Terms Worth Getting Right. 2026.
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[9] Tort Mario. AI Agent Best Practices: Production-Ready Harness Engineering. 2026.
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[10] Addy Osmani. Agent Harness Engineering. 2026.
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[11] Peter Steinberger. You shouldn't be prompting coding agents anymore. 2026.
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[12] Yash Thakker. Loop Engineering: How to Design Coding Agent Loops That Run While You Sleep. 2026.
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[13] Addy Osmani. Loop Engineering. 2026.
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[14] Sydney Runkle. The Art of Loop Engineering. 2026.
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[15] Stanford NLP. DSPy: Programming not prompting Foundation Models. 2024.
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[16] Anthropic. Prompt Caching. 2024.
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[17] Gaurav Garg. Claude Code Deleted a 2.5-Year AWS Production Database: The Full Incident Report. 2025.
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[18] Brandon Gubitosa. What is harness engineering for AI code review & oversight. 2026.
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[19] Aliyun. Model Studio Context Cache. 2026.
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[20] Geoffrey Huntley. Cursed: The unintended consequences of AI code generation. 2025.
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