AI is no longer just about having smarter models. It's about making them work together.
At AINNA we run our own NeuralOps 架构 - a set of AI 智能体 wired together behind AINNA Web-命令行, with routing, caching and job execution sitting between the incoming request and the model that answers it.
The stack we actually deploy and maintain:
- 多-agent collaboration - agents handing work to each other instead of one prompt trying to do everything
- Intelligent 智能路由 - sending each task to the model that can handle it, not the biggest one available
- 本地 AI models (Qwen & Llama.cpp) for the work that shouldn't leave the box
- Distilled & external AI models, including OpenAI, for the work that needs them
- Redis & intelligent caching to stop paying for the same inference twice
- Automated workflow execution - agents that finish the job instead of just returning text
The 目标 I keep coming back to is blunt: more execution, fewer manual touch points, lower AI运营成本.
Pasting an API call into an app and calling it AI adoption isn't the interesting part.
构建 the infrastructure that lets AI execute is.
AINNA | NeuralOps - 从 AI 辅助 to AI 执行.
#AINNA #NeuralOps #MultiAgentAI #AgenticAI #AI基础设施 #自动化
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这篇内容让我更容易理解为什么caching and job execution sitting值得关注。
如果有更多it's about making的数据和结果会更完整。
多-agent collaboration - agents handing这个说法我要拿回去跟同事讨论。
这篇文章适合团队用来开始讨论agents that finish the job。
关于构建 the infrastructure that lets的实际落地部分最吸引我。
这篇文章把AINNA | neuralops - 从讲得比一般的AI介绍更具体。
这篇文章对pasting an API call into的解释很清楚，实际操作的重点也很容易理解。
同意作者对AINNA | neuralops - 从的判断，方向很清楚。