摘要：很多人对构建 AI Agent 感到兴奋，但最终却因为一切听起来太抽象或被过度宣传而陷入困境。如果你真的想认真打造你的第一个 AI Agent，这里有一条实际可行的路径。这不是又一个理论，而是我多次用来构建可用 Agent 的流程。
这是最近 X 上比较流行的一个帖子——来自 Reddit——见原文
内容是如何构建第一个 AI Agent，通过循环——模型 → 工具 → 结果 → 模型，来构建 Agent 。
对，很简单。算是对 Agent 的一点祛魅——即使 Manus 等通用的 Agent，本质也是这么一个小的 Loop 。
I’ve seen a lot of people get excited about building AI agents but end up stuck because everything sounds either too abstract or too hyped. If you’re serious about making your first AI agent, here’s a path you can actually follow. This isn’t (another) theory it’s the same process I’ve used multiple times to build working agents.
很多人对构建 AI Agent 感到兴奋，但最终却因为一切听起来太抽象或被过度宣传而陷入困境。如果你真的想认真打造你的第一个 AI Agent，这里有一条实际可行的路径。这不是又一个理论，而是我多次用来构建可用 Agent 的流程。
Pick a very small and very clear problem. Forget about building a “general agent” right now. Decide on one specific job you want the agent to do. Examples: – Book a doctor’s appointment from a hospital website – Monitor job boards and send you matching jobs – Summarize unread emails in your inbox. The smaller and clearer the problem, the easier it is to design and debug.
选择一个非常小且非常清晰的问题。现在不要想着构建“通用 Agent”。确定你希望 Agent 完成的一个具体任务。举例：——在医院网站预约医生 ——监控招聘网站并推送匹配职位 ——总结收件箱里未读邮件。问题越小越清晰，设计和调试就越容易。
Choose a base LLM. Don’t waste time training your own model in the beginning. Use something that’s already good enough. GPT, Claude, Gemini, or open-source options like LLaMA and Mistral if you want to self-host. Just make sure the model can handle reasoning and structured outputs, because that’s what agents rely on.
选择一个基础大模型。刚开始不要浪费时间训练自己的模型，直接用已经足够好的现成模型。GPT、Claude、Gemini，或者如果你想自托管可以用 LLaMA、Mistral 等开源选项。只要确保模型能处理推理和结构化输出，因为 Agent 依赖这些能力。
Decide how the agent will interact with the outside world. This is the core part people skip. An agent isn’t just a chatbot but it needs tools. You’ll need to decide what APIs or actions it can use. A few common ones: – Web scraping or browsing (Playwright, Puppeteer, or APIs if available) – Email API (Gmail API, Outlook API) – Calendar API (Google Calendar, Outlook Calendar) – File operations (read/write to disk, parse PDFs, etc.)
决定 Agent 如何与外部世界交互。这是很多人忽略的核心部分。Agent 不只是聊天机器人，它需要工具。你需要决定它能用哪些 API 或执行哪些操作。常见选项：——网页爬取或浏览（Playwright、Puppeteer 或现成 API） ——邮箱 API（Gmail、Outlook） ——日历 API（Google、Outlook） ——文件操作（读写磁盘、解析 PDF 等）。
Build the skeleton workflow. Don’t jump into complex frameworks yet. Start by wiring the basics: – Input from the user (the task or goal) – Pass it through the model with instructions (system prompt) – Let the model decide the next step – If a tool is needed (API call, scrape, action), execute it – Feed the result back into the model for the next step – Continue until the task is done or the user gets a final output.
搭建骨架工作流。不要急着用复杂框架，先把基础流程串起来：——用户输入（任务或目标） ——用系统提示传递给模型 ——让模型决定下一步 ——需要工具时（API 调用、爬取、操作），就执行 ——把结果反馈给模型进入下一步 ——直到任务完成或用户得到最终输出。
This loop - model --> tool --> result --> model is the heartbeat of every agent.
这个循环——模型 → 工具 → 结果 → 模型，是每个 Agent 的核心。
Add memory carefully. Most beginners think agents need massive memory systems right away. Not true. Start with just short-term context (the last few messages). If your agent needs to remember things across runs, use a database or a simple JSON file. Only add vector databases or fancy retrieval when you really need them.
谨慎添加记忆。很多新手认为 Agent 一开始就需要庞大的记忆系统，其实不然。先用短期上下文（最近几条消息）即可。如果 Agent 需要跨会话记忆，可以用数据库或简单的 JSON 文件。只有在真正需要时再加向量数据库或复杂检索。
Wrap it in a usable interface. CLI is fine at first. Once it works, give it a simple interface: – A web dashboard (Flask, FastAPI, or Next.js) – A Slack/Discord bot – Or even just a script that runs on your machine. The point is to make it usable beyond your terminal so you see how it behaves in a real workflow.
给它包上一个可用的界面。刚开始用命令行就行。等工作流跑通后，加一个简单界面：——网页仪表盘（Flask、FastAPI、Next.js） ——Slack/Discord 机器人 ——或者只是在本地运行的脚本。关键是让它能在真实流程中使用，不仅限于终端。
Iterate in small cycles. Don’t expect it to work perfectly the first time. Run real tasks, see where it breaks, patch it, run again. Every agent I’ve built has gone through dozens of these cycles before becoming reliable.
小步迭代。不要指望第一次就完美运行。先用真实任务跑一遍，发现问题就修补，再跑一遍。每个我做过的 Agent 都经历了几十次这样的循环才变得可靠。
Keep the scope under control. It’s tempting to keep adding more tools and features. Resist that. A single well-functioning agent that can book an appointment or manage your email is worth way more than a “universal agent” that keeps failing.
控制好范围。很容易不断加工具和功能，但要克制。一个能流畅预约或管理邮件的 Agent，比一个经常出错的“万能 Agent”更有价值。
The fastest way to learn is to build one specific agent, end-to-end. Once you’ve done that, making the next one becomes ten times easier because you already understand the full pipeline.
最快的学习方式是从头到尾做一个具体的 Agent。一旦你完成了这个流程，之后再做其他 Agent 会容易十倍，因为你已经掌握了整个流程。
来源：正正杂说
