What Exactly is AI Engineering? Demystifying Context, Harness, Loop, Eval & Graph
Author: Miles Ma @miles_mazy
Format: Course Article
Est. Read Time: 8 min
Author: Miles Ma @miles_mazy•Date: Sep 17, 2026•Read Time: 8 min
The AI community has coined too many buzzwords recently: Context, Harness, Loop, Graph, Eval... It is indeed easy to get overwhelmed.
Here is my bottom line: From an engineering and software delivery perspective, all these "Engineerings" boil down to one core thing: Prompts.
Except today's prompt is no longer just a few sentences typed into a chatbox.
It includes system constraints, task references, tool descriptions, historical results, failure reflections, and acceptance criteria. As long as the model reads it and uses it to make its next judgment, it is part of the prompt.
The Hard Part of Agents: Getting Things Truly Done
Building an agent demo is simple. Give it a task and a few tools, and it will search the web, read files, and proudly tell you it finished.
Put it into real production, and things fall apart immediately. It repeats completed steps, invokes wrong tools, quits midway, or gets stuck in infinite retry loops.
Engineering is solving these exact realities: what the model should see at each turn, which tools are permissible, how it should adapt after failures, and when to halt for human approval.
Architecture Blueprint: The AI Agent Engineering Stack
From deterministic code boundaries to multi-layer engineering pillars, culminating in dynamic prompt synthesis:
Loop Engineering: Adapting Based on Outcomes
Agent 循环机制与结构化反馈模拟器
“如果反馈只有一句‘失败了，请重试’，模型很可能原样再做一次。”
当前任务目标 (Task Mission)代码刹车上限: 最大 2 轮
计算 2026 年 Q1 营业额同比增幅 (API 期望数值浮点数，模型初次传入带百分号的字符串)
1第 1 轮执行 · 调用接口 calculate_growth(prev="100.5M", curr="128.2M")
执行遇阻环境执行结果: 后端抛出 422: Invalid parameter type.
送回模型的系统反馈 (System Feedback):{"failed_step": 1, "error": "Type mismatch", "fix_hint": "把字符串 \"100.5M\" 转换为物理数值 100500000.0 再传参"}
模型下一步决策思考 (Model Reasoning):收到精确定位！第 1 步参数类型错误，只需要剥离单位 \"M\" 并转为纯浮点数。
2第 2 轮执行 · 修正后调用 calculate_growth(prev=100500000.0, curr=128200000.0)
成功闭环环境执行结果: 接口返回 200 OK: {"growth_rate": "+27.56%"}
送回模型的系统反馈 (System Feedback):物理断言验证通过，产物符合预期。
模型下一步决策思考 (Model Reasoning):计算完成！营业额同比增长 27.56%，成功交付最终报表！
点击“执行下一步”观察反馈如何影响模型
Code Guards Boundaries, Prompts Guide Reasoning
Decision Matrix: Code Boundary vs Prompt Reasoning
代码管边界 vs 提示词管判断：工程职责判定矩阵
“没有代码兜底，Agent 会越界狂奔；没有提示词，Graph 只剩一堆空节点。”
场景 #1
防止死循环与请求数爆炸
限制 Agent 最多连续调用外部工具 10 轮，达到阈值必须硬终止。
场景 #2
理解非标准用户模糊意图
从用户的自然语言“帮我挑一台适合大学生剪视频的轻薄本，预算6000”中提取核心参数。
场景 #3
破坏性危险 SQL 拦截与删除库防护
拦截任何 DROP TABLE、TRUNCATE、DELETE 缺少 WHERE 条件的高危写操作。
场景 #4
工具返回字段选择与下一阶段行动决策
拿到搜索回来的 5 篇网页摘要后，综合判断当前信息是否足够写一份研报。
场景 #5
API 密钥与数据库凭据管理
保管 OpenAI API Key、Stripe 支付密钥，确保在任何日志和输出中不外泄。
场景 #6
代码生成时的语气风格与架构设计权衡
根据企业既有前端规范，在 Vue 3 和 React 之间选择更符合老系统的技术栈并编写组件。
AI 工程化解构沙盘 & 动态 Prompt 透视台
Interactive Studio“这些 Engineering 拆到最后，核心还是提示词——只是不仅是聊天框里那句话。”
Context Engineering · 上下文工程
这一轮到底给模型看什么？塞得少失忆，塞得多迷失
代码管边界 (Code Boundary)
持久化会话快照、检索相关切片、计算当前剩余 Token 预算、敏感数据物理脱敏
提示词管判断 (Prompt Reasoning)
基于当前任务目标，引导模型在有限视窗内聚焦核心变量，忽略历史杂音
落地鸿沟：玩具 Demo vs 生产级落地
❌ 业余做法 (Toy Approach)每一轮把所有历史对话、全部报错与万字检索结果无脑打包拼接塞给 LLM。
✅ 工业级解法 (Production Grade)按阶段分流：规划时只给目标与工具契约；重试时只注入失败原因与已完成步骤；大文件摘要入库后仅传语义向量。
运行时动态 Prompt 组装透视 (Payload Preview)
// [Context Engineering 动态装配示例] <system_rules> 当前任务阶段: 阶段2-数据提取 已消耗 Token: 2,410 / 8,000 上限 可用上下文: [仅保留近 2 轮核心决策 + 3 条结构化工具结果] 历史任务摘要: 用户已确认查询 2026 年 Q1 财报，已成功定位 PDF 索引。 </system_rules>
I am Miles. I built 20,000 followers in a month and wrote three articles with over 1 million impressions in a single week. I opened up my entire agent development process to demystify this once and for all.
