01
输入
Raw requests enter with context, identifiers, documents or structured records.
技术架构 Paper
NeuralOps 架构
This paper explains how AINNA routes work through parsing, AI, deterministic validation and action 层. The goal is not to send everything to the largest model, but to keep each step visible, auditable and fit for purpose.
02
路由
智能路由 selects the cheapest safe path before the LLM is used.
03
Specialised 解析器
解析器s turn messy inputs into versioned structures that can be validated.
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AI 决策 Point
The model handles language-heavy or reasoning-heavy work when rules are not enough.
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独立系统
确定性 business logic executes outside the model to keep repeatable work auditable.
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验证
规则 checks, schema checks and business constraints stop unsafe outputs.
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操作
已批准 actions move to downstream 系统s, workflows or human review.
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日志记录
Every step leaves an 审计追踪 for review, debugging and governance.
Visible explanation
| Layer | 角色 | Why it matters |
|---|---|---|
| 输入 | 采集 user request and operational context. | Reduces ambiguity before the model is involved. |
| 路由 | Choose parser, rules or model path. | Avoids unnecessary 模型调用. |
| 验证 | 检查 schema, logic and business rules. | 停止s invalid outputs from moving downstream. |
| 操作 | Trigger workflow, report or human review. | 保留s human authority where required. |
| 日志记录 | 商店 traceable evidence of each step. | 支持s auditability and debugging. |
AINNA's implementation is intentionally practical: input first, routing second, model only when useful, and 确定性检查 before action.
Interpretation
NeuralOps is AINNA's operating pattern for 智能路由, detached execution and accountable AI-assisted work. It is not a claim that every problem should be solved without a model; it is a claim that the model should not be the only control point.
局限性
- 复杂推理 still needs a model.
- 规则 must be maintained as operations change.
- 日志 prove process 历史, not perfect correctness.
- 人类 review remains necessary for high-risk actions.
引用信息
Suggested citation: AINNA. "NeuralOps 架构." AINNA 研究, 2026. Canonical URL: https://ainna.bond/research/neuralops-architecture/
