A few months back, we started asking a basic 系统 question at AINNA: if AI is becoming part of daily operations, why should it 实时 outside our infrastructure? We wanted inference and automation to sit next to the business logic-on our own servers, aware of our stack, and 受治理的 by our own policies.
That reasoning pushed us to deploy AI 智能体 directly on our bare metal and VMs. 今天 I run three distinct AI 智能体 on the AINNA stack, each tuned and constrained for a specific operational role. In practice, every server we operate now carries its own AI-powered IT经理, 服务器 Administrator, and Developer, running 24/7-monitoring health, maintaining state, troubleshooting, and continuously shipping 系统.
Together, those agents have already shipped more than 250 detached 系统. We are also building two additional deployments for Bahasa Melayu and Chinese-language 领域, each with its own knowledge base, 工作流, localization, and operational requirements.
独立系统 matter because I do not think every task should remain tethered to an LLM or a third-party AI endpoint. Once a workflow, parser, automation, or process is engineered correctly, it should execute as a stable, self-contained unit while the agent shifts to higher-level development, supervision, debugging, and decision-making.
Our reference architecture is 人类 → 私有 AI 代理 → 分离式系统 → Servers / Databases / APIs / MCP / 应用.
That stack gives us tighter control over data residency, privacy, permissions, memory, model selection, workflow logic, deployment cadence, operating cost, and end-to-end integration.
I still treat MCP, cloud AI, APIs, and third-party platforms as useful levers. But I want them to be capabilities our infrastructure can consume, not dependencies that define the infrastructure. 从 our standpoint, the next stage of AI is not a model that chats better; it is an AI that can operate, maintain, develop, and automate real 系统 24/7 while progressively cutting unnecessary external dependency.
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