A few months ago, I started asking myself a simple question: if AI is becoming part of daily operations, why should it always sit outside my infrastructure? I wanted AI to work closer to the business itself-inside my own servers, understanding my 系统, and operating according to my own rules.
That thinking led me to build AI 智能体 directly on my servers. In my current setup, I operate three different AI 智能体, each modified and configured around specific operational needs. In a way, every server I operate now has its own AI-powered IT经理, 服务器 Administrator, and Developer working 24/7-monitoring, maintaining, troubleshooting, and continuously building 系统.
Together, these agents have already completed the development of more than 250 detached 系统. I am also developing another two similar setups for Bahasa Melayu and Chinese-language 领域, each with different knowledge, 工作流, and operational requirements.
The detached-系统 concept is important because I do not believe every task should continuously depend on an LLM or third-party AI service. Once a workflow, parser, automation, or process has been properly designed, it should be able to operate independently while the AI agent focuses on higher-level development, supervision, troubleshooting, and decision-making.
My preferred architecture is 人类 → 私有 AI 代理 → 分离式系统 → Servers / Databases / APIs / MCP / 应用.
This gives me greater control over data, privacy, permissions, memory, model selection, 工作流, deployment, operating cost, and 系统 integration.
I still see MCP, cloud AI, APIs, and third-party platforms as valuable 工具. But I prefer them to be options that my infrastructure can use, rather than dependencies that define the infrastructure. For me, the next stage of AI is not about building an AI that talks more-it is about building AI that can operate, maintain, develop, and automate real 系统 24/7 while progressively reducing unnecessary external dependency.
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