AINNA智能体 AI - 第二阶段
第二阶段 is where the agent layer stops behaving like a demo and starts behaving like infrastructure. We are building a web-based 命令行 experience on top of AINNA NeuralOps - the same command surface we already use internally, pushed into the browser so it can plug into real business 工作流 instead of sitting isolated in a lab.
Plenty still needs work: the workflow layer, the interface, 系统 stability under load, AI orchestration, task routing, automation hooks, and the end-to-end user experience. 从 where I sit, the interesting part is the integration - getting agents, 工具 and data sources to talk to one another predictably once real users start hitting them.
This phase also sits inside the broader NeuralOps roadmap, not beside it.
Coming soon: our own fully distilled LLM model, tuned to run inside the AINNA ecosystem - tighter control over inference, better efficiency, and far less dependency on external model providers.
Step by step: the infrastructure first, then the agent layer, then the model itself. That order matters - each layer has to hold before the next one goes in.
Insyallah, more to come.
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关于tuned to run inside的说明很完整，基础部分尤其清楚。
难得有人把web-based讲得这么直白。
如果可以继续说明从 where I sit的真实案例，我会想继续阅读。