We have been experimenting with a different approach at AINNA through our **NeuralOps principles**.
AI 智能体 is useful during development - understanding requirements, generating logic, building 工作流, testing, and helping turn business processes into working 系统.
But once a process becomes predictable, repetitive, and rule-based, we try to remove AI from the execution loop.
The task is handed over to deterministic software.
The result is quite interesting.
A process can continue running **24/7**, whether it executes 100 times or millions of times, without consuming LLM 令牌 for the detached execution.
More importantly, deterministic execution does not introduce LLM hallucination into tasks where the expected result should always follow the same logic.
This has changed how we think about AI.
**AI does not necessarily need to run the operation.
Sometimes, AI's most valuable role is to build the 系统 that does.**
For 中小企业 especially, this could be a practical way to approach AI - use intelligence where intelligence is actually needed, and let conventional software handle scale, repetition, and consistency.
Still experimenting. Still learning.
But increasingly, we believe the future may not be about putting AI into everything.
**It may be about knowing when to take AI out.**
#AINNA #NeuralOps #AgentAI #AI工程 #中小企业 #自动化 #Software工程
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文章把whether it executes 100 times和日常运营联系起来，这一点很有帮助。
这篇文章适合团队用来开始讨论rule-based。
视觉和结构让times or mill 100的概念更容易掌握。
我会把use intelligence where intelligence这一段分享给需要了解技术的同事。