Complex industrial world, will need complex digital world to be compatible. The target is to design a digital system could help industrial organization to adapt to complex industrial environments. The first step is to design for industrial warehousing scenarios.
This document defines a general Industrial AI OS architecture for industrial manufacturing.
The system is not a simple AI plugin for traditional WMS / MES / ERP, nor a loose collection of digital employees. It is a:
- Industrial operating system built on a unified fact layer
- Intelligent decision system structured around a state–process closed loop
- Multi-agent system whose roles are generated through Agent DNA
- Industrial organizational operating system governed through human–AI co-governance
The first target application is: industrial warehouse management.
Industrial AI OS is a digital organizational infrastructure that reconstructs an industrial enterprise’s sensing, judgment, coordination, execution, and optimization capabilities into a continuously operating layered multi-agent system.
This system is not a single business application. It is:
- An industrial organizational operating system
- It uniformly manages states, tasks, resources, exceptions, permissions, and escalation paths.
- A layered agent runtime system
- Agents at different layers are responsible for problems at different granularities, time scales, and accountability boundaries.
- An evolvable role-generation system
- Role agents are not hand-built one by one; they are generated through progressive differentiation from a general agent core using Agent DNA.
The system addresses the following problems in complex industrial operations:
- Fragmented information and lack of unified real-time situational awareness
- High coordination cost across roles and departments
- Exception handling that depends too heavily on experienced personnel and reacts too slowly
- Processes that can be standardized but cannot dynamically coordinate complex reality
- Operational dependence on supervisors’ tacit knowledge and manual dispatching
- Traditional software that lacks continuous optimization and evolvability
The goal is not simply to replace a few roles. It is to:
upgrade an enterprise’s ability to handle complex industrial operations from dependence on human organizations to dependence on a computable, governable, and evolvable layered agent system.
The system must be hierarchical rather than a flat network of agents.
Why:
- Local problems should be handled by local subsystems
- Upper layers should handle only aggregated issues and cross-domain conflicts
- Exceptions should escalate by layer
- Information should not spread globally without boundaries
Each business domain should operate autonomously at high frequency, while cross-domain collaboration happens at lower frequency.
Examples:
- Inbound domain closes its own loop
- Outbound domain closes its own loop
- Inventory domain closes its own loop
- Cross-domain interaction exchanges only the necessary aggregate states and contract-level information
The system does not “freely search for answers.” It operates by:
- Identifying the current state
- Identifying the target state
- Finding the gap
- Matching the appropriate process
- Driving the system from the current state toward the target state
- If something can be rule-based, do not hand it to a general agent
- If something can be algorithmized, do not hand it to natural-language chat
- If something can be transactional, do not hand it to fuzzy reasoning
Agents are responsible for:
- Judgment
- Coordination
- Explanation
- Escalation
- Local decisions
Lower-level services are responsible for:
- Computation
- Optimization / solving
- Data writes
- Device invocation
- Transactional execution
Humans are not external users. They are governance nodes inside the system.
The system handles:
- High-frequency
- Repetitive
- Structured
- Computable
- Traceable problems
Humans handle:
- Goals
- Boundaries
- Accountability
- Value conflicts
- High-risk decisions
- Complex exceptions
The system should support:
- Template inheritance
- Local mutation
- Canary / phased rollout by version
- Business-level selection
- Preservation of intermediate stable forms
A recommended architecture is “eight layers + one transverse layer + one evolution mechanism.”
This layer is the real industrial environment faced by the system, including:
- Orders
- Materials
- Inventory
- Storage locations
- Docks
- Equipment
- AGVs / forklifts / robotic arms
- Workers and shifts / teams
- Customer commitments
- Supply exceptions
- Quality status
- Time windows
- On-site events
The system exists not to manage data for its own sake, but to influence and improve the operation of the real industrial world.
This layer transforms the industrial objective world into trustworthy digital facts.
It includes:
- Master data
- Order ledgers
- Inventory ledgers
- Equipment states
- Task execution receipts
- Barcode / scan events
- Sensor events
- Interface data from ERP / MES / WMS / TMS / PLC / RCS
- Human confirmations
- Audit logs
Principles:
- There is only one factual world across the whole system
- Agents must not maintain private realities
- All key states must be traceable, replayable, and auditable
This layer organizes facts into a computable state space.
Core state objects:
- Order state
- Inventory state
- Location state
- Task state
- Resource state
- Equipment state
- Quality state
- Exception state
- Human takeover state
- SLA risk state
Aggregate states:
- Inbound congestion index
- Outbound fulfillment risk
- Inventory confidence level
- Equipment load rate
- Wave completion forecast
- Exception backlog severity
This layer answers:
What exactly is happening now?
This layer decomposes complex operational reality into manageable runtime units.
Typical cases:
- An inbound order
- A receiving discrepancy
- A replenishment request
- A wave
- A cycle count task
- An inventory discrepancy event
- A device failure
- A quality hold
- An urgent order insertion request
- A cross-domain conflict
Each case contains:
- Current state
- Target state
- Lifecycle
- Responsible domain
- SLA
- Escalation path
- Related resources
- Decision history
- Audit records
This layer also carries:
- Standard processes
- Exception processes
- Escalation processes
- Compensation processes
- Rollback processes
- Human takeover processes
This layer answers:
What step is this problem currently in, and how should it advance next?
This layer provides deterministic capabilities and is not anthropomorphized.
It includes:
- Rule engines
- Wave solvers
- Route optimizers
- Replenishment threshold engines
- Schedulers
- OCR / visual recognition
- Notification services
- API / RPA connectors
- Reporting services
- Explanation services
- Device control interfaces
Principles:
- Rules, algorithms, and transactional capabilities sink into this layer
- Agents execute by calling this layer
- This is the tooling substrate, not a collection of digital employees
This layer hosts role-oriented agents. These are not hard-coded roles, but phenotypic role instances generated from Agent DNA.
Examples:
- ASN validation agent
- Dock scheduling agent
- Receiving discrepancy classification agent
- Putaway strategy agent
- Inventory health agent
- Discrepancy root-cause agent
- Count-triggering agent
- Location governance agent
- Wave planning agent
- Replenishment trigger agent
- Picking task assignment agent
- Loading sequence agent
- Exception triage agent
- Root-cause analysis agent
- Quality hold coordination agent
- Customer complaint traceability agent
Characteristics of role agents:
- They only see states within their responsibility boundary
- They only call tools within their permission scope
- They are accountable for a limited set of KPIs
- They escalate according to protocol when facing conflicts, low confidence, or high risk
This layer is composed of domain supervisor agents and acts as the middle skeleton of the system.
Typical roles:
- Inbound supervisor agent
- Inventory supervisor agent
- Outbound supervisor agent
- Equipment coordination supervisor agent
- Quality exception supervisor agent
Responsibilities:
- Domain KPI monitoring
- In-domain resource balancing
- In-domain task reallocation
- Risk aggregation
- In-domain exception adjudication
- Output of aggregated domain state upward
- Allocation of goals and constraints downward
This layer answers:
How should this business domain run as a whole?
This is the top coordination layer.
Typical roles:
- Warehouse master control agent
- Site operations agent
- Manufacturing operations master control agent
Responsibilities:
- Cross-domain priority arbitration
- Cost vs. SLA trade-offs
- Total resource constraint coordination
- Top-level escalation of major exceptions
- Distribution of operating goals
- Strategy adjustment recommendations
- Delivery of management views to decision-makers
This layer answers:
What trade-offs should the system make for enterprise-level goals?
This layer cuts across all others.
It defines:
- Which actions execute automatically
- Which actions are recommendation-only
- Which cases require human confirmation
- Which cases must escalate to a human
- Who may take over
- How control returns to the system after takeover
- How decisions are explained
- How audit trails are recorded
Humans are modeled as internal governance nodes rather than external “users.”
Human roles include at least:
- Frontline operators
- Team leaders / supervisors / dispatchers
- Managers / business owners
- Experts / rule maintainers
Agent DNA is the generative grammar for role formation. It is not a prompt.
Defines what outcomes an agent is responsible for.
Typical fields:
- Primary goals
- Secondary goals
- KPI weights
- Optimization bias
Defines what state objects an agent can observe and at what granularity.
Typical fields:
- Observable objects
- Read scope
- State views
- Refresh mode
- Staleness tolerance
Defines what an agent is allowed to do.
Typical fields:
- Allowed actions
- Forbidden actions
- Action modes: auto / suggest-only / approval-required
Defines role-specific cognitive abilities.
Typical fields:
- Reasoning skills
- Classification skills
- Planning skills
- Explanation profile
Defines what tools and services the agent can call.
Typical fields:
- Tool bindings
- Invocation modes
- Tool constraints
Defines limits, escalation conditions, and non-delegable boundaries.
Typical fields:
- Risk level
- Escalation triggers
- Human approval requirements
- Hard limits
Defines how the agent interacts with runtime components, other agents, and humans.
Typical fields:
- Input channels
- Output channels
- Message protocol
- Human interaction mode
Defines how the agent is evaluated.
Typical fields:
- Online metrics
- Business metrics
- Safety metrics
- Override rate
- Rollback rate
The minimal unit is not a bare LLM. It is:
Agent Cell = General Core + DNA + Contract
Provides shared capabilities:
- Language understanding
- Tool invocation framework
- General planning
- Safety constraints
- Memory structure
- Explanation ability
Defines what the role is supposed to become.
Binds the role to a concrete deployment context.
Typical contract fields:
- Site / tenant scope
- Runtime mode
- Permission scope
- Resource scope
- SLA boundary
- Audit requirements
- Fallback mode
The system’s main runtime loop is:
- Sense facts from the industrial world
- Update the unified state model
- Identify the gap between the current state and the target state
- Select or generate the right process in the case layer
- Instantiate or invoke the appropriate role agents
- Call skills and tools to execute
- Receive field receipts and human feedback
- Escalate to higher layers or humans when confidence is low, risk is high, or rules conflict
- Write audit and evaluation records
- Feed outcomes back into template selection and version evolution
Evolution of shared capabilities such as tool invocation, explanation, safety, and memory.
Formation of domain templates such as warehousing, execution, quality, or equipment coordination.
Further differentiation from domain templates into role templates such as ASN validation, wave planning, or replenishment triggering.
Instantiation of the same role template under different sites, customers, equipment conditions, and service commitments.
Runtime adaptation under fixed DNA and contracts. This is not DNA mutation.
What is inherited:
- DNA structure
- Process templates
- Escalation rules
- Tool permissions
- Evaluation metrics
- Explanation patterns
Allowed mutations include:
- Thresholds
- Tool combinations
- Prompt / explanation templates
- Weights
- Escalation gates
Disallowed online mutations include:
- Fact semantics
- Permission baselines
- Audit structures
- Human approval requirements for high-risk actions
Selection is based on business outcomes, such as:
- Fulfillment rate
- Acceptance rate
- False-positive and false-negative rate
- Human override rate
- Rollback rate
- Cross-domain conflict rate
- Exception closure time
The system should retain a library of stable intermediate templates rather than only a single “best” template.
The key engineering objects are:
AgentTemplateAgentInstanceDNAStateObjectEventCaseTaskResourcePolicyToolBindingEscalationAuditRecordHumanDecision
These objects form the engineering skeleton of the Industrial AI OS.
ERP remains the source of:
- Orders
- Commitments
- Financial and business constraints
- Master business boundaries
These systems remain responsible for:
- Transaction execution
- On-site control
- Inventory and execution records
- Device actuation and feedback
Industrial AI OS is responsible for:
- State aggregation and interpretation
- Process generation and dynamic orchestration
- Role-agent collaboration
- Cross-domain coordination
- Exception escalation
- Human–AI co-governance
- Strategy optimization
It is primarily a coordination and runtime layer above and across traditional execution systems.
The first deployment should focus on warehouse management because it has:
- Clear object boundaries
- High event density
- Strong cross-role coordination requirements
- Frequent and measurable exceptions
- Strong need for both local autonomy and cross-domain coordination
The initial warehouse domains should be:
- Inbound
- Inventory
- Outbound
- Exception / quality
- Equipment and resource scheduling
Start with the smallest stable loops:
- Inbound closed loop
- Outbound closed loop
- Exception closed loop
Prioritize:
- ASN validation agent
- Dock scheduling agent
- Putaway strategy agent
- Wave planning agent
- Replenishment trigger agent
- Exception triage agent
- High-risk actions should begin as recommendation-only
- All key actions must be explainable, reversible, and auditable
- Build stable in-domain loops before building cross-domain coordination
- Start with conservative governance, then gradually increase delegation
The core of a general Industrial AI OS is not “letting a few AIs help people do work.” It is:
building a layered digital organizational system that can carry an industrial enterprise’s sensing, judgment, coordination, execution, escalation, and evolution capabilities.
Its logic can be summarized as:
- Unify facts at the bottom
- Build states in the middle
- Use cases to carry processes
- Use the tools layer for deterministic capabilities
- Use role agents for local responsibility
- Use domain supervisors and master control for hierarchical coordination
- Use human–AI co-governance to ensure trust and deployment viability
- Use Agent DNA and template evolution to achieve replicability, scalability, and continuous optimization
本文档定义一套面向工业制造场景的 Industrial AI OS（工业 AI 操作系统） 总体架构。
该系统不是传统 WMS / MES / ERP 的简单 AI 插件，也不是若干数字员工的松散组合，而是一套：
- 以统一事实层为底座的工业运行系统
- 以状态—过程闭环为骨架的智能决策系统
- 以 Agent DNA 为角色生成机制的多智能体系统
- 以人机共治为治理机制的工业组织操作系统
其首个应用场景为：工业仓储管理。
工业 AI OS 是一套将工业企业原本依赖人来完成的感知、判断、协调、执行与优化能力，重构为由分层智能体系统持续运行的数字组织基础设施。
该系统本质上不是单一业务应用，而是：
- 工业组织操作系统
- 统一管理状态、任务、资源、异常、权限与升级路径
- 分层智能体运行系统
- 让不同层级 agent 负责不同粒度、不同时间尺度、不同责任边界的问题
- 可进化角色生成系统
- 岗位 agent 并非手工逐个开发，而是由通用智能体内核通过 Agent DNA 逐层分化生成
该系统主要解决工业企业在复杂现场中的以下问题：
- 信息碎片化，缺乏统一实时状态认知
- 跨岗位、跨部门协同成本高
- 异常处理依赖经验人员，响应滞后
- 流程可以固化，但无法动态协调复杂现实
- 组织运行过度依赖主管经验和人工调度
- 传统软件缺乏持续优化与可进化能力
系统的目标不是简单替代若干岗位，而是：
把工业企业处理复杂现场的能力，从依赖人类组织，升级为依赖可计算、可治理、可进化的分层智能体系统。
系统必须是分层架构，而不是平面化 agent 网络。
原因：
- 局部问题应由局部子系统处理
- 上层只处理聚合问题和跨域冲突
- 异常应按层级升级
- 信息不应在全局无边界扩散
每个业务域内部高频自治，跨域低频协同。
例如：
- 入库域内部闭环
- 出库域内部闭环
- 库存域内部闭环
- 跨域只交换必要的聚合状态与契约信息
系统运行不是“自由搜索答案”，而是：
- 识别当前状态
- 识别目标状态
- 找出差距
- 匹配过程
- 推动系统从当前状态走向目标状态
- 能规则化的，不交给通用 agent
- 能算法化的，不交给自然语言聊天
- 能事务化的，不交给模糊推理
agent 负责：
- 判断
- 协调
- 解释
- 升级
- 局部决策
底层服务负责：
- 计算
- 求解
- 数据写入
- 设备调用
- 事务执行
人不是系统外用户，而是系统内治理节点。
系统负责：
- 高频
- 重复
- 可结构化
- 可计算
- 可追踪的问题
人负责：
- 目标
- 边界
- 责任
- 价值冲突
- 高风险决策
- 复杂例外
系统应支持：
- 模板继承
- 局部变异
- 版本灰度
- 业务选择
- 中间形态保留
建议采用 “八层一横一机制” 架构。
该层是系统所面对的真实工业环境，包括：
- 订单
- 物料
- 库存
- 库位
- 月台
- 设备
- AGV / 叉车 / 机械臂
- 人员与班组
- 客户承诺
- 供应异常
- 质量状态
- 时间窗口
- 现场事件
说明：
系统不是为了管理数据而存在，而是为了影响和改善真实工业现场的运行。
负责把工业客观世界转成可依赖的数字事实。
包括：
- 主数据
- 订单台账
- 库存台账
- 设备状态
- 作业回执
- 扫码事件
- 传感器事件
- ERP / MES / WMS / TMS / PLC / RCS 接口数据
- 人工确认记录
- 审计日志
原则：
- 全系统只有一个事实世界
- 不允许 agent 维护各自私有现实
- 所有关键状态必须可追踪、可回放、可审计
负责把事实组织成系统可计算的状态空间。
核心状态对象：
- 订单状态
- 库存状态
- 库位状态
- 任务状态
- 资源状态
- 设备状态
- 质量状态
- 异常状态
- 人工接管状态
- SLA 风险状态
聚合状态：
- 入库拥堵指数
- 出库履约风险
- 库存可信度
- 设备负载率
- 波次完成预测
- 异常积压等级
该层回答的问题：
现在到底发生了什么。
该层把复杂现实拆成一个个可处理的运行单元。
典型 case：
- 一张入库单
- 一次收货差异
- 一次补货请求
- 一个波次
- 一张盘点任务
- 一次库存差异事件
- 一次设备故障
- 一次质量锁定
- 一次插单申请
- 一次跨域冲突
每个 case 包含：
- 当前状态
- 目标状态
- 生命周期
- 责任域
- SLA
- 升级路径
- 相关资源
- 决策历史
- 审计记录
该层同时承载：
- 标准流程
- 异常流程
- 升级流程
- 补偿流程
- 回退流程
- 人工接管流程
该层回答的问题：
这个问题现在处于哪一步，下一步如何推进。
该层提供确定性能力，不拟人化。
包括：
- 规则引擎
- 波次求解器
- 路径优化器
- 补货阈值引擎
- 排程器
- OCR / 视觉识别
- 通知服务
- API / RPA 连接器
- 报表服务
- 解释服务
- 设备控制接口
原则：
- 规则、算法、事务能力下沉到该层
- agent 通过调用该层完成执行
- 该层是“工具底座”，不是“数字员工”
该层承载岗位型 agent，但这些 agent 不是手工硬编码角色，而是由 Agent DNA 实例化生成的表型角色。
示例：
- ASN 校验智能体
- 月台排程智能体
- 收货差异判定智能体
- 上架策略智能体
- 库存健康智能体
- 差异归因智能体
- 盘点触发智能体
- 库位治理智能体
- 波次规划智能体
- 补货触发智能体
- 拣选任务分派智能体
- 装车排序智能体
- 异常分流智能体
- 根因分析智能体
- 质量锁定协同智能体
- 客诉追溯智能体
角色智能体特征：
- 只看职责边界内状态
- 只调用权限内工具
- 只对有限 KPI 负责
- 遇到冲突、低置信度或高风险情况时按协议升级
该层由“域主管 agent”构成，是整个系统的中间骨架。
典型角色：
- 入库主管智能体
- 库存主管智能体
- 出库主管智能体
- 设备协调主管智能体
- 质量异常主管智能体
职责：
- 域内 KPI 监控
- 域内资源平衡
- 域内任务重分配
- 风险聚合
- 域内异常裁决
- 向上层输出聚合状态
- 向下层分配目标与约束
该层回答的问题：
这个业务域整体应该如何运行。
该层为系统最高协调层。
典型角色：
- 仓库总控智能体
- 站点经营智能体
- 制造运营总控智能体
职责：
- 跨域优先级裁决
- 成本与 SLA 权衡
- 资源总量约束协调
- 重大异常总升级
- 经营目标下发
- 策略调整建议
- 向管理者输出经营视图
该层回答的问题：
为了企业整体目标，系统全局应该如何取舍。
该层横贯所有层级。
定义内容：
- 哪些动作自动执行
- 哪些动作只能建议
- 哪些情况必须人确认
- 哪些情况必须升级给人
- 谁可以接管
- 接管后如何交还系统
- 如何解释决策
- 如何记录审计轨迹
人被建模为系统内部治理节点，而不是系统外部“用户”。
人类角色至少包括：
- 一线执行人员
- 班组长 / 主管 / 调度
- 经理 / 经营负责人
- 专家 / 规则维护者
Agent DNA 是岗位角色的可遗传生成语法，不只是 skill 与工具包。
一个完整 Agent DNA 至少包含以下 8 类基因：
定义它对什么目标负责。
示例：
- 履约率
- 补货及时率
- 库存准确率
- 异常闭环时长
定义它能感知哪些状态对象、观察哪些窗口。
示例：
- 订单状态
- 库位状态
- 库存可信度
- 设备可用性
- 波次拥堵程度
定义它可执行哪些动作。
示例：
- 建议
- 分派
- 排程
- 锁定
- 升级
- 请求人工确认
定义它拥有哪些认知和判断能力。
示例：
- 差异识别
- 异常归因
- 优先级排序
- 风险判断
- 策略解释
定义它可调用哪些服务和接口。
示例：
- WMS API
- MES API
- 规则引擎
- 优化器
- OCR 服务
- 通知服务
定义它的职责边界与权限边界。
示例：
- 不能直接改库存
- 超过阈值必须升级
- 不得跳过质量锁定
- 不得绕过人工审批
定义它如何与人、与 case、与其他 agent 交互。
示例：
- 只能通过 case 发起协作
- 异常升级必须结构化输出
- 输出必须包含理由与证据
定义如何评价该 agent。
示例：
- 建议采纳率
- 误判率
- 回滚率
- 人工接管率
- SLA 影响
- 异常缩短时长
系统中 agent 的最小单元不是裸模型，而是：
Agent Cell = 通用内核 + DNA + 合约
负责：
- 通用理解
- 任务分解
- 工具调用框架
- 通用安全
- 通用解释能力
- 通用记忆机制
决定该智能体“长成什么角色”。
负责把 DNA 实例化为实际运行节点，定义：
- 输入
- 输出
- 权限
- SLA
- 审计要求
- 资源限制
- 升级规则
整套 Industrial AI OS 的运行逻辑可统一为以下主循环：
- 感知事实
- 更新状态模型
- 识别当前状态与目标状态的差距
- 在 case 层匹配或生成过程
- 实例化相关岗位 agent
- 调用技能与工具执行
- 获取执行回执与人工反馈
- 低置信度 / 高风险 / 规则冲突时升级
- 回写审计记录与评估指标
- 反馈到模板选择与版本进化
这个闭环体现的是：
- 先做状态描述
- 再做过程生成
- 再把过程结果回写成新状态
- 形成持续适应
系统进化不是生产环境自由突变，而是版本化、可审计、可选择的受控演化。
更新通用能力：
- 推理框架
- 工具调用框架
- 安全机制
- 解释能力
- 记忆管理
沉淀领域模板：
- 仓储领域模板
- 制造执行领域模板
- 质量领域模板
- 设备协调领域模板
在领域模板之上分化岗位模板：
- ASN 校验模板
- 波次规划模板
- 补货触发模板
- 异常分流模板
岗位模板结合实际环境约束，形成站点级实例模板。
运行期在既定 DNA 与合约边界内进行局部策略选择，不改变基因本体。
继承经过验证的：
- DNA 模板
- 流程模板
- 工具配置
- 升级规则
- 评估方法
允许局部、受控变化：
- 阈值
- 提示模板
- 优先级权重
- 工具组合
- 升级门槛
通过业务结果进行选择，例如：
- 履约率
- 误判率
- 回滚率
- 人工接管率
- 跨域冲突率
- 异常处理时长
保留稳定中间形态，而不仅是最终模板。包括：
- 通用内核模板
- 领域模板
- 岗位模板
- 站点模板
系统建议统一抽象以下对象：
- State：状态
- Event：事件
- Case：问题 / 流程实例
- Task：动作单元
- Resource：资源
- Rule / Policy：规则与策略
- Agent Template：角色模板
- Agent Instance：运行中的角色实例
- Human Role：系统内治理节点
- Escalation：升级路径
- Audit Trail：审计轨迹
这些对象构成整个 Industrial AI OS 的工程骨架。
负责：
- 经营计划
- 财务逻辑
- 主业务约束
- 订单承诺
负责：
- 现场执行
- 事务处理
- 控制指令
- 记录与回执
负责：
- 状态汇总与解释
- 过程生成与动态编排
- 多角色智能体协同
- 跨域协调
- 异常升级
- 人机共治
- 运行优化
- 模板进化
因此，该系统可位于现有工业系统之上或之间，成为：
组织协调与智能运行层。
通用 Industrial AI OS 的首个落地方向为：工业仓储管理。
建议优先划分为以下业务域：
- 入库域
- 库存域
- 出库域
- 异常与质量域
- 设备与资源调度域
首期可围绕以下最小闭环开展：
- ASN 校验
- 收货差异识别
- 上架策略生成
- 波次规划
- 补货触发
- 异常分流
- 人工升级与接管机制
建议先做三个域：
- 入库域
- 出库域
- 异常域
- ASN 校验 agent
- 上架策略 agent
- 波次规划 agent
- 补货触发 agent
- 异常分流 agent
- 高风险动作先只建议，不自动执行
- 所有关键动作必须可解释、可回滚、可审计
- 先做域内闭环，再做跨域协同
- 先保守治理，再逐步放权
通用 Industrial AI OS 的核心，不是“让几个 AI 帮人做事”，而是：
构建一套能够承载工业企业感知、判断、协调、执行、升级与进化能力的分层数字组织系统。
其总体逻辑可归纳为：
- 底层统一事实
- 中层建立状态
- 用 case 承载过程
- 用工具层承载确定性能力
- 用角色 agent 承载局部职责
- 用域主管与总控承载层级协调
- 用人机共治保证可信与可落地
- 用 Agent DNA 和模板进化实现可复制、可扩张、可持续优化