During my time as an IT经理, building a proper web-based application usually meant coordinating a full team.
A typical project could involve a 项目 Manager, 商业 Analyst, UI/UX Designer, 前端 Developer, 后端 Developer, Database Engineer, 解决方案 Architect, QA 测试机, DevOps Engineer, and 安全 Engineer.
That experience taught me one important lesson:
A good 系统 is not built by putting everything into one technology. It is built by assigning each task to the right layer.
This is one of the principles behind NeuralOps.
今天, AI 智能体 can handle a large portion of planning, coding, analysis, testing, debugging, documentation and decision support. But I do not believe every task should be sent to an LLM.
In NeuralOps, repetitive and deterministic workloads are moved into 分离式系统 - conventional 系统 such as PHP services, MySQL, schedulers, parsers, queues, validation engines and automation scripts.
The AI handles the work that requires intelligence.
The detached 系统 handles the work that requires consistency, speed and reliability.
This changes the development model significantly.
Previously:
大 technical team → many specialised functions
今天:
技术 领先 → AI智能体 → 分离式系统
A smaller team can now coordinate functions that previously required an entire development department, while keeping infrastructure cost, token usage and operational complexity under control.
For me, this is where AI becomes truly useful for 中小企业.
Not by replacing every 系统 with AI, but by combining human experience, AI reasoning and reliable conventional infrastructure into one practical architecture.
That is the direction we are building with AINNA NeuralOps.
#NeuralOps #AINNA #AgenticAI #AIAutomation #SystemDevelopment #SoftwareArchitecture #中小企业 #DigitalTransformation #AIInfrastructure #自动化
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这篇文章把repetitive and deterministic workloads讲得比一般的AI介绍更具体。
总结部分让web-based的重点更加清楚。
关于coding, analysis, testing, debugging的风险和限制还可以再展开，不过基础说明已经很好。
我对商业 analyst, UI/UX designer, 前端还有问题，但文章已经提供了很好的起点。 值得再看一遍。
我特别喜欢AI 智能体 can handle这一部分，内容没有把实施过程说得太简单。