I built AI agents for 20+ startups this year. Here is the engineering roadmap to actually getting started.
I run an automation agency and I have built custom agent architectures for over 20 startups this year alone. I see beginners in this sub constantly asking which no-code wrapper they should use to build a fully autonomous employee. They want to skip the engineering.
我经营一家自动化代理公司，仅今年一年就为超过 20 家初创公司构建了定制代理架构。我看到这个版块里的新手们不断询问应该使用哪个无代码封装库来构建完全自主的员工。他们想跳过工程开发环节。
This is why most of them fail. Building a reliable agent is not about writing a long prompt. It is about systems engineering. If you want to build agents that solve real business problems you need to respect the hierarchy of skills. Do not touch a model until you understand the layers underneath it.
这就是大多数智能体失败的原因。构建一个可靠的智能体并非编写冗长的提示，而是系统工程。如果你想构建能够解决实际业务问题的智能体，就必须尊重技能层级。在理解模型底层结构之前，切勿贸然触碰模型。
Here is the realistic roadmap and how it actually plays out in production.
以下是切实可行的路线图以及它在生产中的实际执行情况。
Phase 1 Data Transport 第一阶段数据传输
You cannot build an agent if you do not understand how data moves.
如果你不了解数据是如何流动的，你就无法构建代理。
- Python. It is the non-negotiable standard. Learn it.
Python。它是不容置疑的标准。学它。 - REST APIs. You need to understand how to read documentation and authenticate a request. If you cannot manually send a request to get data you have no business building an agent.
REST API。你需要了解如何阅读文档并验证请求。如果你连手动发送数据请求都做不到，那就没必要开发代理。 - JSON. This is how machines speak to each other. Learn how to parse it and structure it.
JSON。这是机器之间相互通信的方式。学习如何解析和构建它。
Tutorials show clean data. In reality you will spend 80% of your time handling messy JSON responses and figuring out why an API documentation lied to you. The code that parses the data is more important than the code that generates the text.
教程里展示的数据都很干净。但实际上，你 80%的时间都会花在处理混乱的 JSON 响应和弄明白为什么 API 文档会误导你。解析数据的代码比生成文本的代码更重要。
Phase 2 Storage and Memory 第二阶段存储和内存
An agent without memory is just a text generator.
没有记忆功能的智能体只是一个文本生成器。
- SQL. Structured data is the backbone of business. Learn how to query a database to get absolute facts.
SQL。结构化数据是业务的基石。学习如何查询数据库以获取绝对准确的信息。 - Vector Stores. Understand how embeddings work. This is how software finds context in a pile of unstructured text.
向量存储。了解嵌入的工作原理。这就是软件如何在大量非结构化文本中找到上下文的方式。 - Data Normalization. Bad data means bad outputs. Learn to clean data before you feed it to a model.
数据归一化。糟糕的数据会导致糟糕的输出。在将数据输入模型之前，请先学习如何清洗数据。
Vector databases are not magic. If you dump garbage documents into a vector store the agent will retrieve garbage context. You have to manually clean and chunk your data or the search will fail.
向量数据库并非魔法。如果您将垃圾文档直接存入向量存储，代理程序将检索到垃圾上下文。您必须手动清理和分块数据，否则搜索将失败。
Phase 3 Logic and State 第三阶段逻辑和状态
This is where the actual value lives.
这才是真正价值所在。
- State Management. You need to track where a conversation is. You must carry variables from one step to the next to keep the context alive.
状态管理。你需要追踪对话的进展情况。你必须将变量从一个步骤传递到下一个步骤，以保持上下文的连贯性。 - Function Calling. This is how you give a model the ability to execute code. Learn how to define a function that the software can choose to run.
函数调用。这是赋予模型执行代码能力的方法。学习如何定义一个软件可以选择运行的函数。
The AI does not actually do the work. It simply chooses which function to run. Your Python function does the work. If your function is buggy the best AI in the world cannot save you.
人工智能实际上并不执行任何工作，它只是选择要运行哪个函数。真正执行工作的是你的 Python 函数。如果你的函数存在缺陷，即使是世界上最好的人工智能也无能为力。
Phase 4 Connecting the Model 第四阶段：连接模型
Now you introduce the intelligence layer.
现在引入智能层。
- Context Windows. Understand the limits of short term memory. You cannot feed a model an entire book every time.
上下文窗口。了解短期记忆的局限性。你不可能每次都把一整本书的内容输入到模型中。 - Routing. Stop asking one prompt to do everything. Build a router that classifies the intent and sends it to a specialized function.
路由。不要让一个请求执行所有操作。构建一个路由器，对意图进行分类，并将其发送到专门的函数。 - Error Handling. The model will fail. The API will time out. You need code that catches the failure and retries automatically.
错误处理。模型可能会失败。API 可能会超时。你需要编写代码来捕获错误并自动重试。
In production models hallucinate constantly. You cannot trust the output blindly. You need to write code that validates the response before showing it to the user.
生产环境中的模型经常会出现各种问题。你不能盲目信任输出结果。你需要编写代码来验证响应，然后再将其显示给用户。
Phase 5 Reliability 第五阶段可靠性
- Webhooks. How to trigger your agent from the outside world.
Webhooks：如何从外部触发您的代理。 - Background Jobs. How to run your agent on a schedule.
背景工作。如何按计划运行您的代理。 - Logging. If you do not know why your agent failed you did not build a system. You built a slot machine.
日志记录。如果你不知道代理程序失败的原因，那你构建的不是系统，而是老虎机。
Clients do not care if you used the latest model. They only care if the system runs every single day without breaking. Reliability is the only metric that matters.
客户并不关心你是否使用了最新型号。他们只关心系统能否每天稳定运行，不出现故障。可靠性才是唯一重要的指标。
Stop looking for shortcuts. Learn the primitives. It is just engineering.
别想着走捷径。先学好基础知识。这才是工程学。