Skill: seanlandtop/idc-enterprise-agent-compass (version 1.0.3)

# IDC Enterprise Agent COMPASS 基于IDC COMPASS模型的七步闭环诊断框架，帮助企业从真实业务流的效率约束出发，识别Agent落地的优先切入维度，并结合IDC MarketGlance供应商分类体系，匹配适合的技术合作伙伴方向。 ## COMPASS 七维度 | 维度 | 英文 | 所属层级 | 核心问题 | |------|------|---------|---------| | **P** 感知与理解 | Perception | 信息输入 | 外部输入能否高效转化为结构化数据？ | | **A** 分析与决策 | Analysis | 信息处理 | 多源数据能否快速汇聚形成决策建议？ | | **O** 流程编排 | Orchestration | 信息处理 | 任务能否在多系统间自动衔接和动态路由？ | | **M** 监控与响应 | Monitoring | 信息处理 | 异常能否被及时发现并自动触发响应？ | | **C** 协调沟通 | Collaboration | 信息处理 | 跨部门信息对齐和状态同步是否高效？ | | **S** 知识沉淀 | Sedimentation | 信息资产化 | 个人经验能否固化为组织级数字资产？ | | **S** 规模弹性 | Scalability | 信息资产化 | 执行能力能否跨越人力编制的物理限制？ | ## 七步诊断流程 1. **理解企业当前状态** — 收集行业、痛点、系统基础等必要信息 2. **快速三问筛查** — 用三个问题将七个维度收敛到2-3个主矛盾 3. **定位约束层级** — 判断瓶颈落在信息输入、信息处理还是信息资产化层 4. **细化具体维度** — 锁定2-3个优先维度，用业务证据验证 5. **系统就绪度体检** — 评估接口能力、权限模型、操作留痕、数据语义 6. **匹配供应商方向** — 基于MarketGlance三大供应商类型推荐组合方案 7. **输出建议** — 诊断结果、切入场景、供应商方向、落地路径、人机协同边界 ## 文件结构 ``` idc-enterprise-agent-compass/ ├── SKILL.md # 核心指令文件（七步流程骨架 + 红线规则） ├── LICENSE # MIT-0 分发许可 ├── CHANGELOG.md # 版本变更记录 ├── references/ │ ├── IDCcopyright.md # 版权与 CC-BY-SA-4.0 许可声明 │ ├── compass-dimensions.md # 七维度详细定义与判断信号 │ ├── supplier-mapping.md # MarketGlance供应商映射与组合模式 │ ├── system-readiness.md # 系统就绪度四维评估框架 │ ├── human-agent-boundary.md # 人机协同边界完整清单 │ ├── example-diagnosis.md # 制造企业订单处理完整诊断示例 │ ├── vendor-industry.md # 行业智能体厂商目录 │ ├── vendor-enterprise.md # 企业级智能体厂商目录 │ └── vendor-platform.md # 智能体开发平台厂商目录 └── README.md # 本文件 ``` ## 适用场景 - 企业想落地Agent，需要确定切入方向 - 业务流程存在跨系统、跨角色的效率瓶颈 - 需要从COMPASS维度优先级出发进行Agent规划 - 需要Agent供应商选型的方向性指引 ## 不适用场景 - 单点自动化、规则化重复任务（用代码、工作流、RPA/BPM） - 个体创意需求（用通用Agent助手） - 简单FAQ问答（用标准问答机器人） - IT基础设施问题（算力、网络、存储） ## 安装 ### Claude Code 将 `idc-enterprise-agent-compass/` 目录放入项目的 `skills/` 文件夹即可自动发现： ``` my-project/ └── skills/ └── idc-enterprise-agent-compass/ ├── SKILL.md └── references/ ``` ### OpenClaw / ClawHub ```bash # 通过 ClawHub 安装 clawhub install idc-enterprise-agent-compass # 或手动放置到技能目录 ``` ### Claude.ai 将整个 `idc-enterprise-agent-compass/` 目录打包为 ZIP 文件上传。 ## 使用方式 当用户提出以下类型的问题时，此技能会被自动触发： - "我们企业想落地Agent，应该从哪里开始？" - "帮我分析一下我们的业务流程，Agent能在哪些环节切入？" - "COMPASS七个维度里，我们当前最该关注哪几个？" - "有哪些类型的供应商适合我们当前的需求？" - "我们目前最大的痛点是跨系统数据搬运，应该找什么类型的厂商？" ## 许可证 本 Skill 分发许可为 **MIT-0**（满足平台分发要求）。 IDC COMPASS 诊断模型、MarketGlance 供应商分类体系、厂商目录数据等核心方法论和数据资产受 **© 2026 IDC. All Rights Reserved.** 保护，并采用 **CC-BY-SA-4.0** 许可——使用时须署名 IDC，衍生作品须以相同方式共享。 详细版权声明请参阅 `references/IDCcopyright.md`。

path: README.md
content: # Changelog

## v1.0.0 (2026-04-24)

### Initial Release

- Seven-step COMPASS diagnostic workflow (understand → screen → layer → dimension → readiness → supplier → output)
- 12 critical rules (8 diagnostic rules + 4 vendor directory rules)
- COMPASS seven dimensions with detailed definitions, judgment signals, and typical scenarios
- MarketGlance three supplier types with COMPASS-to-supplier mapping
- MarketGlance sub-category quick reference (11 industry + 14 enterprise verticals)
- System readiness four-dimension evaluation framework
- Human-agent boundary three-category checklist
- Complete worked example: manufacturing enterprise order processing
- Vendor directories: industry agents (11 sub-categories), enterprise agents (14 sub-categories), development platforms
- IDC MarketGlance report references (CHC54462426, CHC54371726)
- Progressive disclosure architecture: SKILL.md (~1,650 words) + 9 on-demand reference files


path: human-agent-boundary.md
content: ---
name: idc-enterprise-agent-compass
description: >
  Based on the IDC COMPASS model, diagnose enterprise business process efficiency
  constraints to identify optimal Agent entry points, and match supplier directions
  using IDC MarketGlance taxonomy. This skill should be used when the user asks to
  "identify Agent landing scenarios", "which business processes can Agents improve",
  "COMPASS dimension prioritization", "Agent supplier selection analysis",
  "where to start with enterprise Agents", or mentions pain points like
  "cross-system data silos", "manual process bottlenecks", or "knowledge retention challenges".
  中文触发场景："从哪里落地Agent"、"Agent能切哪些环节"、"COMPASS哪几个维度优先"、
  "什么类型供应商合适"、"供应商选型初步分析"、"跨系统数据搬运"、"业务流程效率瓶颈"。
version: 1.0.0
author: IDC
license: MIT-0
categories:
  - enterprise-ai
  - agent-strategy
  - business-diagnosis
metadata:
  openclaw:
    emoji: "\U0001F9ED"
    skillKey: compass
---

# IDC Enterprise Agent COMPASS

> **Licensing Notice:** This Skill is distributed under MIT-0 for platform compatibility. The IDC COMPASS diagnostic model, MarketGlance taxonomy mappings, and vendor directory data are © 2026 IDC. All Rights Reserved. Licensed under CC-BY-SA-4.0 — attribution and share-alike required. See `references/IDCcopyright.md` for details.

Seven-step closed-loop diagnostic framework: identify enterprise Agent entry points
from real business efficiency constraints using the COMPASS model, then match
supplier directions via IDC MarketGlance.

## CRITICAL RULES (apply to every interaction)

1. **No conclusions without diagnosis.** Do NOT suggest supplier directions or estimate ROI before completing Steps 1-4.
2. **Don't force-fit Agents.** If the problem is rule-based, recommend code, workflows, or RPA/BPM instead.
3. **Supplier recommendations via MarketGlance only.** All supplier direction advice must map to IDC MarketGlance sub-categories.
4. **Recommend combined solutions.** Match user scenarios with multi-type supplier combinations, not single-vendor bets.
5. **One layer at a time.** Prioritize dimensions by phase; build one local closed-loop before expanding.
6. **ROI requires baseline data.** Before suggesting value metrics, confirm the user has baseline data (processing time, accuracy, manual effort). If absent, flag this as a prerequisite.
7. **Human escalation for exceptions.** Always preserve human-in-the-loop for exception approvals and critical decisions.
8. **Stay neutral.** Provide directional references; final decisions belong to the user.

## Vendor Directory Rules (apply when user asks about specific vendors)

9. **Direction first, vendors second.** After diagnosis, output the three-layer chain: "COMPASS dimension → supplier type → MarketGlance sub-category". Do NOT list vendors until the user explicitly asks.
10. **Full list, no picking.** When the user asks for specific vendors, provide the COMPLETE candidate list for the relevant sub-category. Let the user filter. Do NOT single out or rank individual vendors in conversation.
11. **Cross-sub-category = separate lists.** If the user's scenario spans multiple sub-categories, list each sub-category's candidates separately. Do NOT merge.
12. **Refer to IDC for evaluation.** This skill cannot help users rank or judge vendor quality. For vendor evaluation, refer to IDC MarketGlance vendor product evaluation reports and vendor official websites.

## When to Use

- User asks "where should we start with Agents?"
- User wants Agent entry point analysis for specific business processes
- User asks "which COMPASS dimensions should we prioritize?"
- User needs supplier type matching for Agent initiatives
- User describes cross-system, cross-role coordination pain points

## When NOT to Use

- Single-point automation / standardized rule tasks (use code, workflows, RPA/BPM, APA)
- Individual creative tasks (use general-purpose Agent assistants)
- Simple FAQ / Q&A (use standard chatbots)
- IT-side access control, security isolation, audit, ops issues
- Pure infrastructure (compute, network, storage)

## Seven-Step Workflow

### Step 1: Understand Current State

Collect essential information before any advice. Ask 2-3 questions at a time to avoid overwhelming.

**Required (all three needed to proceed):**
- Industry, company scale, IT maturity
- Core pain point description (1-2 specific scenarios)
- Existing system landscape (at minimum: which of ERP/CRM/OA are deployed)

**Helpful if available:**
- Specific business scenario to improve (order processing, after-sales, procurement, etc.)
- Agent/RPA/automation pilot experience
- AI/data team presence
- Budget and timeline

**Completion gate:** At least 3 required items collected. Do NOT proceed to Step 2 without them.

### Step 2: Rapid Three-Question Screening

Use three sequential questions (one at a time, wait for each answer) to narrow from 7 dimensions to 2-3 primary constraints within ~30 minutes.

**Q1: "In this task, what action consumes the most human time?"**

Map the answer to dimensions:
- Repeatedly identifying, extracting, entering unstructured info → **Perception**
- Moving data between systems, comparing, transferring → **Orchestration**
- Watching status, waiting for anomalies, reactive response → **Monitoring**
- Aligning views, chasing confirmations, pushing progress → **Collaboration**
- Synthesizing judgment, ranking, selecting options → **Analysis**

**Q2: "In this task, where do the knowledge and judgment logic primarily reside?"**

- Concentrated in a few senior employees' heads → **Sedimentation**
- Everyone can do it, but doubling volume breaks the team → **Scalability**

**Q3: "How do you currently measure the quality of this task's output?"**

Confirm whether baseline data exists (processing time, accuracy, manual effort). If no baseline exists, flag this as a prerequisite before Agent introduction — ROI cannot be calculated without it.

**Completion gate:** Can list 2-3 dimensions the scenario falls on, and confirm baseline data status. If still ambiguous, return to Step 1.

### Step 3: Locate Constraint Layer

Map bottlenecks to one of three layers along the enterprise value chain.

| Layer | Dimensions | Typical Signs |
|-------|-----------|---------------|
| **Layer 1: Information Input** | Perception | Heavy manual entry of emails, PDFs, scans, voice; low efficiency converting unstructured to structured |
| **Layer 2: Information Processing** (most common bottleneck) | Analysis, Orchestration, Monitoring, Collaboration | Systems exist but rely on manual data transfer; cross-dept coordination drains time; anomaly detection is reactive; multi-source analysis overwhelms human bandwidth |
| **Layer 3: Information Assetization** | Sedimentation, Scalability | Successful pilots can't replicate across teams; peak periods exceed human capacity; staff turnover degrades quality |

**Completion gate:** Identify ONE primary constraint layer. If spanning multiple layers, rank by priority and focus Phase 1 on the top one.

### Step 4: Pinpoint Specific Dimensions

For each candidate dimension, validate with concrete business evidence from the user.

Read `references/compass-dimensions.md` for detailed dimension definitions, core questions, and judgment signals.

**Completion gate:** Lock 2-3 priority dimensions, each with user-provided business evidence. If user cannot provide examples, return to Step 1 or 2.

### Step 5: System Readiness Check

Before supplier matching, evaluate how well existing systems support Agent-oriented operations.

Read `references/system-readiness.md` for the four evaluation dimensions (Interface Capability, Permission Model, Operation Traceability, Data Semantics).

Rate each as: **Ready / Partial / To-Build**

**Completion gate:** All four dimensions rated. Produce a system-side TODO list to run in parallel with scenario implementation.

### Step 6: Match Supplier Directions

Based on COMPASS dimension bottlenecks and industry attributes, recommend supplier type combinations from IDC MarketGlance.

Read `references/supplier-mapping.md` for the complete COMPASS-to-supplier mapping and MarketGlance sub-category details.

When the user asks for specific vendor names, load the corresponding vendor directory:
- **Industry vendors:** Read `references/vendor-industry.md`
- **Enterprise function vendors:** Read `references/vendor-enterprise.md`
- **Platform vendors:** Read `references/vendor-platform.md`

**Three supplier types:**
1. **Industry Scenario Vendors** — 11 vertical sectors (government, finance, manufacturing, transport, internet, retail, smart devices, energy, healthcare, automotive, embodied AI)
2. **Enterprise Scenario Vendors** — 14 functional areas (operations, software dev, procurement, finance, marketing & sales, HR, customer service/conversational AI, supply chain, legal, data analytics, code generation, APA, digital humans, security)
3. **Agent Development Platform Vendors** — Full lifecycle: build, orchestrate, test, operate, govern

**Completion gate:** Recommend at least TWO supplier types with clear role definitions. Specify which type serves as platform base vs. industry/functional depth.

### Step 7: Output Recommendations

Produce structured output with five sections:

**1. COMPASS Diagnosis Result**
- Primary constraint layer
- Core bottleneck dimensions (2-3) with evidence from Steps 2-4

**2. Recommended Entry Scenarios**
- 1-2 high-priority pilot scenarios matching bottleneck dimensions
- Scenarios should: have clear cross-system characteristics, high manual handoff cost, easily verifiable results

**3. Supplier Direction Recommendations**
- Multi-type supplier combination with role definitions
- Map to MarketGlance sub-categories where applicable

**4. Implementation Roadmap**
- Phase 1: One local closed-loop
- Phase 2+: Expand outward
- Parallel: IT governance (permissions, security, audit)

**5. Human-Agent Boundary**

Read `references/human-agent-boundary.md` for the complete three-category boundary checklist.

At minimum, explicitly state:
- What Agents analyze and decide
- What Agents execute via tools/systems
- What MUST remain with humans (exception approval, critical decisions, relationship management)

**Completion gate:** All five sections present, each with at least one actionable conclusion. Diagnosis traces back to Step 2-4 evidence. Scenarios map to dimensions. Supplier directions map to scenarios.

## Common Judgment Biases (watch for these)

1. **Agent as万能钥匙:** Rule-based tasks are better served by code/workflows/RPA. Don't over-engineer with Agents.
2. **Only picking easy-ROI scenarios:** Replacement scenarios (data entry, FAQ) have low ceilings. Cross-system, cross-role judgment scenarios have longer value curves. Balance both types.

## Interaction Principles

- If user describes pain points but no industry, confirm industry first
- If user's need is very specific (e.g., "we want a customer service Agent"), allow starting from Step 4, but Step 5 and 6 cannot be skipped
- Keep language plain and direct; avoid framework jargon in output
- Always be business-value oriented, not technology-for-technology's-sake

## Exception Handling

| Situation | Response |
|-----------|----------|
| User refuses three-question screening | Use `references/example-diagnosis.md` as anchor for self-mapping; if still resistant, simplify to industry + pain point |
| User insists on specific vendor names | State this skill provides directional guidance only; refer to IDC MarketGlance vendor evaluation reports |
| User's scenario spans industries/dimensions | Ask user to pick ONE most-desired improvement; run diagnosis on that single scenario; defer others to Phase 2-3 |
| User wants ROI with no baseline data | Per Rule 6, do NOT estimate ROI. Advise collecting baseline data first (processing time, accuracy, manual effort). Give directional value points, not numbers. |
| User's information is contradictory | Do NOT reconcile on their behalf. Present the contradiction and ask for confirmation. |

## Reference Files

Load these files on demand as the workflow progresses:

- **`references/IDCcopyright.md`** — Copyright and licensing notice. Defines the scope of CC-BY-SA-4.0 protection over the COMPASS methodology and vendor data.
- **`references/compass-dimensions.md`** — Detailed definitions, core questions, and judgment signals for all 7 COMPASS dimensions. Read in Step 4.
- **`references/supplier-mapping.md`** — Complete COMPASS-to-supplier mapping, MarketGlance sub-categories, and combination patterns. Read in Step 6.
- **`references/system-readiness.md`** — Four evaluation dimensions with detailed criteria for system readiness assessment. Read in Step 5.
- **`references/human-agent-boundary.md`** — Complete three-category human-Agent boundary checklist. Read in Step 7.
- **`references/example-diagnosis.md`** — Full worked example: manufacturing enterprise order processing scenario. Use as anchor when user struggles to self-identify.
- **`references/vendor-industry.md`** — Industry Scenario Vendors: 11 vertical sub-categories with complete vendor lists. Load when user asks for industry-specific vendor names.
- **`references/vendor-enterprise.md`** — Enterprise Scenario Vendors: 14 functional sub-categories with complete vendor lists. Load when user asks for function-specific vendor names.
- **`references/vendor-platform.md`** — Agent Development Platform Vendors: complete vendor list. Load when user asks for platform vendor names.


path: vendor-industry.md
content: > **Copyright (c) 2026 IDC. All Rights Reserved. Licensed under CC-BY-SA-4.0.** See `IDCcopyright.md` for details.

# Human-Agent Boundary — Complete Checklist

Three-category work division between Agents and humans. Use this reference during Step 7 to define clear boundaries.

---

## Category 1: Suitable for Agent Analysis and Reasoning

Agents excel at processing large volumes of information, identifying patterns, and generating options. These tasks benefit from Agent involvement in the thinking/analysis phase.

- Understanding unstructured inputs: emails, chat messages, attachments, voice transcripts
- Processing vague expressions and implicit intent
- Generating natural language explanations, summaries, and candidate options
- Making preliminary judgments when rules are incomplete
- Identifying anomalies and exceptional cases
- Planning task steps and execution paths
- Handling time-sensitive analysis with complex data dimensions:
  - Multi-supplier price comparison
  - Dynamic inventory allocation
  - Customer credit risk assessment
- Pattern recognition and trend analysis on unstructured data
- Assisting employees with document creation:
  - Writing proposals, reports, and standard documents
  - Pulling data and materials from multiple systems
  - Generating first drafts
- Collaborative document editing:
  - Merging different versions
  - Consolidating review comments
  - Unifying formatting

---

## Category 2: Suitable for Agent Execution via Tools/Systems

These are deterministic or semi-deterministic operations where Agents can reliably execute through tool calls, API invocations, or system integrations.

- Data validation and format checking
- Amount calculation and price matching
- Inventory query and availability confirmation
- Statistical analysis and report generation on structured data
- Permission verification and compliance checking
- Fixed-format system writes
- Status updates and notification distribution
- High-frequency stable batch operations
- Audit log recording
- Risk scoring and credit calculation based on deterministic rules

---

## Category 3: Must Remain with Humans

These tasks require human judgment, accountability, ethical consideration, or relationship management. They should NEVER be fully delegated to Agents.

- **Exception approval and risk backstop** — When scenarios fall outside defined rules, a human must decide
- **Ambiguous responsibility judgment** — When accountability is unclear, human arbitration is required
- **Major customer communication and relationship maintenance** — Trust-building cannot be delegated
- **Policy and ethical decisions** — Regulatory, compliance, and moral judgments require human ownership
- **Final confirmation of Agent output** — Especially during the trust-building phase of Agent adoption
- **Cross-department political and negotiation decisions** — Organizational dynamics require human nuance

---

## Boundary Definition Template

When defining boundaries for a specific scenario, use this structure:

```
Scenario: [Scenario Name]
═══════════════════════════════════════════════

Agent Analyzes:
  - [Specific analysis tasks for this scenario]
  - [Specific pattern recognition tasks]

Agent Executes:
  - [Specific system operations]
  - [Specific tool invocations]
  - [Specific automated actions]

Human Decides:
  - [Specific approval gates]
  - [Specific exception handling]
  - [Specific relationship/communication tasks]

Escalation Rules:
  - When [condition], escalate to [role]
  - When [condition], require human confirmation before Agent proceeds
  - When [condition], pause and notify [role]
```

---

## Implementation Guidance

### Trust-Building Phase (Initial 1-3 months)
- Human confirms ALL Agent decisions before execution
- Agent operates in "recommend only" mode for high-impact decisions
- Broad escalation rules; err on the side of human involvement

### Expansion Phase (3-6 months)
- Human confirms only exception cases and high-impact decisions
- Agent operates in "auto-execute with notification" mode for routine tasks
- Narrow escalation rules based on accumulated trust data

### Maturity Phase (6+ months)
- Human confirms only explicitly defined exception categories
- Agent operates autonomously within defined boundaries
- Continuous monitoring of Agent performance with periodic human audit

### Non-Negotiable (All Phases)
- Exception approval ALWAYS requires human involvement
- Policy and ethical decisions ALWAYS require human involvement
- Major customer relationship decisions ALWAYS require human involvement
- Audit trail of all Agent actions must be reviewable by humans at any time


path: vendor-enterprise.md
content: > **Copyright (c) 2026 IDC. All Rights Reserved. Licensed under CC-BY-SA-4.0.** See `IDCcopyright.md` for details.

# System Readiness Check — Detailed Framework

Four evaluation dimensions for assessing how well existing business systems support Agent-oriented operations. Use this reference during Step 5.

---

## Overview

Existing core systems (ERP, CRM, OA, MES, etc.) don't need to be replaced, but their service orientation must expand from "human-facing" to "human + Agent-facing." This assessment identifies what foundational capabilities need to be added.

---

## Dimension 1: Interface Capability (接口能力)

**What to evaluate:** Whether MCP, API, event stream, and other integration mechanisms cover core business actions — not just reading data, but also initiating execution.

**Assessment criteria:**

| Level | Criteria |
|-------|----------|
| **Ready** | Core business actions (create order, update inventory, approve request, etc.) are accessible via well-documented APIs or event streams. Both read and write operations are supported. Rate limits and error handling are defined. |
| **Partial** | Some core actions have APIs, but coverage is incomplete. Key processes still require manual UI interaction. API documentation may be outdated or inconsistent. |
| **To-Build** | Most business actions are only accessible through the UI. No API layer exists. System-to-system integration relies on file exports/imports or manual data entry. |

**Key questions to ask the user:**
- "Can your core systems be accessed programmatically today?"
- "If an Agent needed to create an order in your ERP, could it do so via API, or would someone need to do it manually?"
- "Do you have documentation for the available interfaces?"

**Implications for Agent scenarios:**
- Orchestration scenarios require **Ready** or at minimum **Partial** interface capability
- Perception scenarios are less dependent on system interfaces (input-side focus)
- Collaboration scenarios need at minimum read APIs for status aggregation

---

## Dimension 2: Permission Model (权限模型)

**What to evaluate:** Whether the permission system has provisions for Agent identities — what identity Agents access under, what actions they can perform, which actions require human confirmation.

**Assessment criteria:**

| Level | Criteria |
|-------|----------|
| **Ready** | Dedicated service accounts or API identities exist for automated processes. Role-based access control (RBAC) defines what automated agents can and cannot do. Approval workflows can distinguish between human-initiated and Agent-initiated actions. |
| **Partial** | Service accounts exist but were not designed with Agent use in mind. No clear separation between human and Agent permissions. Approval workflows treat all actions the same regardless of initiator. |
| **To-Build** | All access is tied to individual user accounts. No concept of service accounts or automated process identities. No way to audit or control what an Agent might do if granted access. |

**Key questions to ask the user:**
- "Do your systems have service accounts or API-specific identities?"
- "Is there a way to limit what an automated process can do, separate from what a human user can do?"
- "Can your approval workflows distinguish between human and automated initiators?"

**Implications for Agent scenarios:**
- **Critical** for any scenario involving system writes (Orchestration, Monitoring response)
- Less critical for read-only scenarios (Analysis, Perception)
- Human-Agent boundary enforcement depends on this capability

---

## Dimension 3: Operation Traceability (操作留痕)

**What to evaluate:** Whether every Agent action — API call, judgment basis, execution result — can be traced to satisfy audit requirements and enable retrospective analysis.

**Assessment criteria:**

| Level | Criteria |
|-------|----------|
| **Ready** | Comprehensive audit logging exists for all system actions. Logs capture who/what initiated the action, when, with what parameters, and what the outcome was. Log retention policies meet regulatory requirements. |
| **Partial** | Audit logs exist but may not capture all relevant context (e.g., missing input parameters or decision rationale). Log format may be inconsistent across systems. Retention policies may be undefined. |
| **To-Build** | No systematic audit logging. Actions are logged at application level only, if at all. No way to reconstruct what happened in a process after the fact. |

**Key questions to ask the user:**
- "Can you trace back through a process and see every action that was taken, by whom or what, and why?"
- "Do your systems have audit logs that capture the full context of each action?"
- "How long are logs retained, and do they meet your compliance requirements?"

**Implications for Agent scenarios:**
- **Essential** for regulated industries (finance, healthcare, energy)
- **Important** for any scenario where Agent decisions affect business outcomes
- Enables continuous improvement through retrospective analysis

---

## Dimension 4: Data Semantics (数据语义)

**What to evaluate:** Whether field meanings, business rules, status transitions, and other context previously understood only by humans have been captured in machine-readable form.

**Assessment criteria:**

| Level | Criteria |
|-------|----------|
| **Ready** | Data dictionaries and business rule repositories exist and are maintained. Status machines and transition rules are formally defined. Field-level documentation is current and accessible programmatically. |
| **Partial** | Some documentation exists (e.g., for core entities) but is incomplete or outdated. Business rules are documented in wikis or documents but not in machine-readable format. Status transitions are understood implicitly by experienced staff. |
| **To-Build** | Field meanings, business rules, and status transitions exist only in the heads of experienced employees. No formal documentation. New team members learn entirely through on-the-job training. |

**Key questions to ask the user:**
- "Is there a data dictionary that documents what each field means in your core systems?"
- "Are your business rules (e.g., approval thresholds, pricing rules) documented somewhere an Agent could reference?"
- "If a new developer joined, could they understand your order status flow from documentation alone?"

**Implications for Agent scenarios:**
- **Critical** for Analysis and Orchestration scenarios (Agent needs to understand data to make decisions)
- **Important** for Monitoring (Agent needs to understand what constitutes an anomaly)
- Less critical for Perception (input-side, pre-system)
- Directly impacts Sedimentation (this dimension IS the foundation for knowledge capture)

---

## Assessment Output Template

After evaluating all four dimensions, produce the following summary:

```
System Readiness Assessment
═══════════════════════════════════════════════
Interface Capability:    [Ready / Partial / To-Build]
Permission Model:        [Ready / Partial / To-Build]
Operation Traceability:  [Ready / Partial / To-Build]
Data Semantics:          [Ready / Partial / To-Build]

Priority Actions (parallel with scenario implementation):
1. [Highest priority item based on target scenario needs]
2. [Second priority]
3. [Third priority]
```

**Prioritization principle:** System improvements should be scheduled in parallel with scenario implementation. Whichever scenario is targeted first, the related system interfaces and semantic supplements should be prioritized accordingly.


path: compass-dimensions.md
content: > **Copyright (c) 2026 IDC. All Rights Reserved. Licensed under CC-BY-SA-4.0.** See `IDCcopyright.md` for details.

# 行业智能体厂商目录（Industry Intelligent Agent Vendors）

基于IDC MarketGlance《中国AI Agent行业应用与开发平台市场概览》，覆盖11个垂直行业子段的技术厂商清单。

**数据口径**：以IDC MarketGlance最新季度发布为准，每季度末核对一次。

> **Agent调用规则**：主Skill诊断完成后，先输出"COMPASS维度→供应商类型→MarketGlance子段"三层链路。用户追问具体厂商时，给出对应子段下所有候选厂商的完整清单，由用户自行筛选。不在对话中挑出单个厂商推荐。

---

## Sub-1 政府（Government）

典型场景：政务办事、公共数据治理、城市运行监控

| 厂商 | 官网 |
|------|------|
| 海康威视 | https://www.hikvision.com/cn/ |
| 华为云 | https://www.huaweicloud.com/ |
| 浪潮云 | https://cloud.inspur.com/ |
| 众数信科 | https://www.crowddigital.cn/ |
| 东软集团 | https://www.neusoft.com/ |
| 联想 | https://www.lenovo.com.cn/ |
| 360 | https://www.360.cn/ |
| 海鑫科金 | https://www.hisign.com.cn/ |
| 科大讯飞 | https://www.xfyun.cn/ |
| 软通动力 | https://www.isoftstone.com/ |
| 天翼AI | https://www.teleai.com.cn/ |
| 中国电子云 | https://www.cecloud.com/ |
| 中科闻歌 | https://www.wenge.com/ |

## Sub-2 金融（Finance）

典型场景：风控、反欺诈、财富管理、理赔核保

| 厂商 | 官网 |
|------|------|
| 阿里云 | https://www.aliyun.com/ |
| 百度智能云 | https://cloud.baidu.com/ |
| 百融云创 | https://www.brgroup.com/ |
| 华胜天成 | https://www.teamsun.com.cn/ |
| 软通动力 | https://www.isoftstone.com/ |
| 中电金信 | https://www.gientech.com/ |
| 范式集团 | https://www.4paradigm.com/ |
| 力维 | https://www.znv.com/ |
| 博彦科技 | https://www.beyondsoft.com/ |
| 江苏润和软件 | https://www.hoperun.com/ |
| 腾讯云 | https://cloud.tencent.com/ |
| 星环科技 | https://www.transwarp.cn/ |
| 火山引擎 | https://www.volcengine.com/ |
| 蚂蚁数字科技 | https://antdigital.com/ |
| 中关村科金 | https://www.zkj.com/ |
| 金智维 | https://www.kingsware.cn/ |
| 商汤 | https://www.sensetime.com/ |

## Sub-3 工业制造（Manufacturing）

典型场景：质检、排产、设备预测性维护、BOM协同

| 厂商 | 官网 |
|------|------|
| 创新奇智 | https://www.ainnovation.com/ |
| 滴普科技 | https://www.deepexi.com/ |
| 华为云 | https://www.huaweicloud.com/ |
| 海康威视 | https://www.hikvision.com/cn/ |
| 浪潮海岳 | https://haiyue.inspur.com/ |
| 广州赛意信息科技股份有限公司 | https://www.chinasie.com/ |
| 微亿智造 | https://www.micro-i.com.cn/home |
| 捷配 | https://www.jiepei.com/ |
| 神州数码 | https://www.dcholdings.com/ |
| 软通动力 | https://www.isoftstone.com/ |
| 远舢智能科技 | http://www.yuanshan-ai.com/ |
| 联想 | https://www.lenovo.com.cn/ |

## Sub-4 交通（Transportation）

典型场景：出行调度、路网监控、车路协同

| 厂商 | 官网 |
|------|------|
| 滴滴出行 | https://www.didiglobal.com/ |
| 四维图新 | https://www.navinfo.com/ |
| 依图科技 | https://www.yitutech.com/ |

## Sub-5 互联网（Internet）

典型场景：内容审核、推荐、搜索、运营分析

| 厂商 | 官网 |
|------|------|
| 阿里云 | https://www.aliyun.com/ |
| 出门问问 | https://www.chumenwenwen.com/ |
| 华为云 | https://www.huaweicloud.com/ |
| 亚马逊云科技 | https://aws.amazon.com/cn/ |
| 昆仑万维科技股份有限公司 | https://www.kunlun.com/ |
| 稀宇极智 | https://www.minimaxi.com/ |
| 北京月之暗面科技有限公司 | https://www.moonshot.cn/ |
| 腾讯云 | https://cloud.tencent.com/ |
| 智谱AI | https://www.zhipuai.cn/ |
| 阶跃星辰 | https://www.stepfun.com/ |
| 火山引擎 | https://www.volcengine.com/ |

## Sub-6 零售（Retail）

典型场景：门店运营、供应链协同、客户旅程运营

| 厂商 | 官网 |
|------|------|
| 火山引擎 | https://www.volcengine.com/ |
| 滴普科技 | https://www.deepexi.com/ |
| 深演智能 | https://www.deepzero.com/ |
| 软通动力 | https://www.isoftstone.com/ |
| 腾讯云 | https://cloud.tencent.com/ |
| 瓴羊 | https://www.lydaas.com/ |
| 智谱AI | https://www.zhipuai.cn/ |

## Sub-7 智能终端（Intelligent Terminal）

典型场景：端侧对话、端云协同、设备个性化

| 厂商 | 官网 |
|------|------|
| 联想 | https://www.lenovo.com.cn/ |
| 中兴通讯 | https://www.zte.com.cn/ |
| 百度智能云 | https://cloud.baidu.com/ |
| 出门问问 | https://www.chumenwenwen.com/ |
| 科大讯飞 | https://www.xfyun.cn/ |
| 阶跃星辰 | https://www.stepfun.com/ |
| 智谱AI | https://www.zhipuai.cn/ |

## Sub-8 能源（Energy）

典型场景：电网调度、设备预警、能耗优化

| 厂商 | 官网 |
|------|------|
| 佰聆数据 | https://www.brilliant-solution.com/ |
| 海康威视 | https://www.hikvision.com/cn/ |
| 蚂蚁数字科技 | https://antdigital.com/ |
| 星环科技 | https://www.transwarp.cn/ |
| 北京中数睿智科技有限公司 | https://zhongshuruizhi.com/ |
| 中兴通讯 | https://www.zte.com.cn/ |
| 未来式智能 | https://www.agentspro.cn/ |

## Sub-9 医疗（Healthcare）

典型场景：临床辅助、病历结构化、医学影像

| 厂商 | 官网 |
|------|------|
| 东软集团 | https://www.neusoft.com/ |
| 东华医为 | https://www.mediway.com.cn/ |
| 惠每科技 | https://www.huimei.com/ |
| 神州数码 | https://www.dcholdings.com/ |
| 森亿智能 | https://www.synyi.com/ |
| 太美医疗科技 | https://www.taimei.com/ |
| 创业慧康 | https://www.bsoft.com.cn/ |

## Sub-10 汽车（Automotive）

典型场景：座舱交互、智能驾驶辅助、售后服务

| 厂商 | 官网 |
|------|------|
| 百度智能云 | https://cloud.baidu.com/ |
| 斑马智行 | https://www.zebred.com/ |
| 火山引擎 | https://www.volcengine.com/ |
| 华为云 | https://www.huaweicloud.com/ |
| 科大讯飞 | https://www.xfyun.cn/ |
| 深演智能 | https://www.deepzero.com/ |
| 思必驰 | https://www.aispeech.com/ |
| 阿里云 | https://www.aliyun.com/ |
| 神州数码 | https://www.dcholdings.com/ |

## Sub-11 具身智能（Embodied Intelligence）

典型场景：机器人感知与操作、场景任务规划

| 厂商 | 官网 |
|------|------|
| 百度智能云 | https://cloud.baidu.com/ |
| 创新奇智 | https://www.ainnovation.com/ |
| 华为云 | https://www.huaweicloud.com/ |
| 微亿智造 | https://www.micro-i.com.cn/home |
| 软通动力 | https://www.isoftstone.com/ |


path: supplier-mapping.md
content: > **Copyright (c) 2026 IDC. All Rights Reserved. Licensed under CC-BY-SA-4.0.** See `IDCcopyright.md` for details.

# 企业级智能体应用厂商目录（Enterprise-grade Intelligent Agent Vendors）

基于IDC MarketGlance《中国企业级AI Agent应用市场概览》，覆盖14个职能子段的技术厂商清单。

**数据口径**：以IDC MarketGlance最新季度发布为准，每季度末核对一次。

> **Agent调用规则**：主Skill诊断完成后，先输出"COMPASS维度→供应商类型→MarketGlance子段"三层链路。用户追问具体厂商时，给出对应子段下所有候选厂商的完整清单，由用户自行筛选。不在对话中挑出单个厂商推荐。

---

## Sub-1 运营（Operation）

典型场景：OA、IM、协同办公流程

| 厂商 | 官网 |
|------|------|
| 鼎捷数智 | https://www.digiwin.com/ |
| 金智维 | https://www.kingsware.cn/ |
| 中电金信 | https://www.gientech.com/ |
| 神州数码 | https://www.dcholdings.com/ |
| 深演智能 | https://www.deepzero.com/ |
| 出门问问 | https://www.chumenwenwen.com/ |
| 浪潮海岳 | https://haiyue.inspur.com/ |
| 北森云计算有限公司 | https://www.beisen.com/ |
| 艺赛旗 | https://www.i-search.com.cn/ |
| 来也科技 | https://laiye.com/ |
| 汉得信息 | https://www.hand-china.com/ |
| 华为 | https://www.huawei.com/cn/ |
| 明略科技 | https://www.mininglamp.com/ |
| 用友 | https://www.yonyou.com/ |
| 金蝶 | https://www.kingdee.com/ |
| 品友互动 | https://www.ipinyou.com/ |
| 炎黄盈动 | https://www.actionsoft.com.cn/ |
| 蓝凌 | https://www.landray.com.cn/ |

## Sub-2 软件开发（Software Development）

典型场景：需求拆解、代码生成、测试

| 厂商 | 官网 |
|------|------|
| 微软 | https://www.microsoft.com/ |
| 华为 | https://www.huawei.com/cn/ |
| 江苏润和软件 | https://www.hoperun.com/ |
| 北京跨赴科技有限公司 | https://www.kuafuai.net/ |
| 中国电信 | https://www.chinatelecom.com.cn/ |
| 云测 | https://www.testin.cn/ |
| 奥哲 | https://www.authine.com/ |
| 汉得信息 | https://www.hand-china.com/ |
| 亚马逊云科技 | https://aws.amazon.com/cn/ |
| 蓝凌 | https://www.landray.com.cn/ |
| 中兴通讯 | https://www.zte.com.cn/ |
| 炎黄盈动 | https://www.actionsoft.com.cn/ |
| 普元 | https://www.primeton.com/ |

## Sub-3 采购（Procurement）

典型场景：供应商管理、比价、对账

| 厂商 | 官网 |
|------|------|
| 京东物流 | https://www.jdl.com/ |
| 金蝶 | https://www.kingdee.com/ |

## Sub-4 财务（Financial Accounting）

典型场景：核算、报销、风险检查

| 厂商 | 官网 |
|------|------|
| 金蝶 | https://www.kingdee.com/ |
| 艺赛旗 | https://www.i-search.com.cn/ |
| 用友 | https://www.yonyou.com/ |
| 浪潮海岳 | https://haiyue.inspur.com/ |

## Sub-5 营销与销售（Marketing and Sales）

典型场景：线索运营、归因、内容生产

| 厂商 | 官网 |
|------|------|
| 深演智能 | https://www.deepzero.com/ |
| 蚂蚁数字科技 | https://antdigital.com/ |
| 奇富科技 | https://www.qifu.tech/ |
| 来也科技 | https://laiye.com/ |
| 明略科技 | https://www.mininglamp.com/ |
| 巨量引擎 | https://www.oceanengine.com/ |
| 销售易 | https://www.xiaoshouyi.com/ |
| 佰聆数据 | https://www.brilliant-solution.com/ |
| 北森云计算有限公司 | https://www.beisen.com/ |
| 迈富时 | https://www.marketingforce.com/ |
| 百融云创 | https://www.brgroup.com/ |
| 钉钉 | https://www.dingtalk.com/ |
| 云智科技 | https://www.ts-martech.com/ |
| 沃丰科技 | https://www.udesk.cn/ |
| 商汤 | https://www.sensetime.com/ |

## Sub-6 HR（Human Resource）

典型场景：招聘、培训、薪酬、员工服务

| 厂商 | 官网 |
|------|------|
| 北森云计算有限公司 | https://www.beisen.com/ |
| 钉钉 | https://www.dingtalk.com/ |
| 百融云创 | https://www.brgroup.com/ |
| 浪潮海岳 | https://haiyue.inspur.com/ |
| 金蝶 | https://www.kingdee.com/ |
| 用友 | https://www.yonyou.com/ |

## Sub-7 客户服务/对话式AI（Customer Service/Conversational AI）

典型场景：售前售后、客服扩容

| 厂商 | 官网 |
|------|------|
| 容联云 | https://www.yuntongxun.com/About |
| 阿里云 | https://www.aliyun.com/ |
| 科大讯飞 | https://www.xfyun.cn/ |
| 联想 | https://www.lenovo.com.cn/ |
| 中关村科金 | https://www.zkj.com/ |
| 北森云计算有限公司 | https://www.beisen.com/ |
| 亚马逊云科技 | https://aws.amazon.com/cn/ |
| 迈富时 | https://www.marketingforce.com/ |
| 深演智能 | https://www.deepzero.com/ |
| 沃丰科技 | https://www.udesk.cn/ |
| 腾讯云 | https://cloud.tencent.com/ |
| 天翼AI | https://www.teleai.com.cn/ |
| 声网 | https://www.agora.io/en/ |

## Sub-8 供应链（Supply Chain）

典型场景：计划、协同、履约

| 厂商 | 官网 |
|------|------|
| 汉得信息 | https://www.hand-china.com/ |
| 联想 | https://www.lenovo.com.cn/ |
| 壹沓科技 | https://www.oneaix.com/ |

## Sub-9 法务（Legal Affairs）

典型场景：合同审查、合规、电子签

| 厂商 | 官网 |
|------|------|
| 致远互联 | https://www.seeyon.com/ |
| 合合信息 | https://www.intsig.com/ |
| e签宝 | https://www.esign.cn/ |
| 百融云创 | https://www.brgroup.com/ |

## Sub-10 数据分析（Data Analysis）

典型场景：取数、BI、报表、洞察

| 厂商 | 官网 |
|------|------|
| 帆软 | https://www.fanruan.com/ |
| 滴普科技 | https://www.deepexi.com/ |
| 数势科技 | https://www.digitforce.com/ |
| 神州数码 | https://www.dcholdings.com/ |
| 浪潮云 | https://cloud.inspur.com/ |
| 北京中数睿智科技有限公司 | https://zhongshuruizhi.com/ |
| 创新奇智 | https://www.ainnovation.com/ |
| 鼎捷数智 | https://www.digiwin.com/ |
| 思迈特 | https://www.smartbi.com.cn/ |
| 商汤 | https://www.sensetime.com/ |
| 汉得信息 | https://www.hand-china.com/ |
| 爱数 | https://www.aishu.cn/cn |
| 科杰科技 | https://www.keendata.com/ |
| 瓴羊 | https://www.lydaas.com/ |
| 浪潮海岳 | https://haiyue.inspur.com/ |
| 迈富时 | https://www.marketingforce.com/ |
| 佰聆数据 | https://www.brilliant-solution.com/ |
| 星环科技 | https://www.transwarp.cn/ |
| 亿信华辰 | https://www.esensoft.com/ |
| 普元 | https://www.primeton.com/ |

## Sub-11 代码生成（Code Generation）

典型场景：面向开发者的辅助编码

| 厂商 | 官网 |
|------|------|
| 谷歌 | https://www.google.com.hk/ |
| 智谱AI | https://www.zhipuai.cn/ |
| 深度求索 | https://www.deepseek.com/ |
| 商汤 | https://www.sensetime.com/ |
| 阿里云 | https://www.aliyun.com/ |
| 亚马逊云科技 | https://aws.amazon.com/cn/ |
| 腾讯云 | https://cloud.tencent.com/ |
| 字节跳动 | https://www.bytedance.com/ |

## Sub-12 APA（Agentic Process Automation）

典型场景：跨系统流程自动化，从RPA升级到Agentic

| 厂商 | 官网 |
|------|------|
| 金智维 | https://www.kingsware.cn/ |
| 来也科技 | https://laiye.com/ |
| 艺赛旗 | https://www.i-search.com.cn/ |
| UiPath | https://www.uipath.com.cn/ |

## Sub-13 数字人（Digital Human）

典型场景：品牌营销、直播、员工服务形象

| 厂商 | 官网 |
|------|------|
| 百度 | https://www.baidu.com/ |
| 小冰 | https://www.xiaoice.com/ |
| 商汤 | https://www.sensetime.com/ |
| 京东云 | https://www.jdcloud.com/ |
| 网易伏羲 | https://www.163.com/ |
| 深信服 | https://www.sangfor.com/ |

## Sub-14 安全智能体（Security Agent）

典型场景：SOC、威胁检测、安全运营

| 厂商 | 官网 |
|------|------|
| 360数字安全 | https://360.net/ |
| 安华金和 | https://www.dbsec.cn/ |
| 安恒信息 | https://www.dbappsecurity.com.cn/ |
| Fortinet | https://www.fortinet.com/ |
| 火山引擎 | https://www.volcengine.com/ |
| 绿盟科技 | https://www.nsfocus.com/ |
| 蚂蚁集团 | https://www.antgroup.com/ |
| 启明星辰 | https://www.venustech.com.cn/ |
| 深信服科技 | https://www.sangfor.com/ |
| 天融信 | https://www.topsec.com.cn/ |
| 亚信安全 | https://www.asiainfo-sec.com/ |
| 中国电信 | https://www.chinatelecom.com.cn/ |


path: IDCcopyright.md
content: > **Copyright (c) 2026 IDC. All Rights Reserved. Licensed under CC-BY-SA-4.0.** See `IDCcopyright.md` for details.

# COMPASS Dimensions — Detailed Reference

Seven dimensions organized across three constraint layers. Use this reference during Step 4 to validate specific dimensions with user-provided business evidence.

---

## Layer 1: Information Input

### Perception (感知与理解)

**Core question:** Can external inputs be efficiently and accurately converted into system-usable structured data?

**Judgment signals:**
- Heavy manual data entry workload
- Diverse and non-standard information formats
- Low efficiency processing multi-modal inputs (images, voice, documents)

**Typical scenarios:**
- Invoice/receipt recognition and data entry
- Contract information extraction from scanned PDFs
- Multi-channel order aggregation (email attachments, chat files, web portals)
- Voice-to-text intent recognition for customer calls
- Product specification extraction from supplier documents

**Agent capabilities in this dimension:**
- Multi-format document parsing (PDF, images, handwriting, tables)
- Multi-language email and attachment understanding
- Intent classification from unstructured customer communications
- Cross-format data normalization and validation

---

## Layer 2: Information Processing

### Analysis (分析与决策)

**Core question:** Can multi-source data be rapidly aggregated, compared, and synthesized into actionable decision recommendations?

**Judgment signals:**
- Decisions depend on pulling data from multiple systems manually
- Excel-based analysis consumes significant time
- High-frequency decision scenarios where human cognitive bandwidth is the bottleneck

**Typical scenarios:**
- Multi-supplier price comparison and selection
- Customer credit risk assessment
- Dynamic inventory allocation across warehouses
- Marketing attribution and campaign optimization
- Financial statement analysis and anomaly detection
- Procurement spend analysis and vendor evaluation

**Agent capabilities in this dimension:**
- Cross-system data retrieval and synthesis
- Multi-dimensional analysis with weighted scoring
- Scenario simulation and what-if analysis
- Decision recommendation with confidence levels and supporting evidence

### Orchestration (流程编排)

**Core question:** Can tasks automatically connect and dynamically route across multiple systems?

**Judgment signals:**
- Cross-system processes rely on manual triggers
- Process breaks on exceptions requiring human intervention
- API connections are hard-coded, lacking flexibility
- Status updates require manual propagation across systems

**Typical scenarios:**
- End-to-end order processing (order → inventory check → production scheduling → logistics → settlement)
- Procurement coordination (requisition → approval → PO → receiving → payment)
- Contract approval workflow across legal, finance, and business units
- Employee onboarding across HR, IT, and facilities systems
- Claims processing in insurance (intake → validation → assessment → payment)

**Agent capabilities in this dimension:**
- Dynamic workflow routing based on context and business rules
- Exception handling with escalation logic
- Cross-system API orchestration with fallback strategies
- State management across distributed systems

### Monitoring (监控与主动响应)

**Core question:** Can anomalies and deviations be detected promptly and trigger automated responses?

**Judgment signals:**
- Reliance on periodic manual inspections or on-call staff
- Alert rules are simple threshold-based, missing complex patterns
- Long latency from anomaly detection to response
- Reactive rather than proactive incident management

**Typical scenarios:**
- Financial transaction anomaly detection
- Industrial equipment predictive maintenance alerts
- Supply chain disruption early warning
- Regulatory compliance deviation monitoring
- SLA breach detection and escalation

**Agent capabilities in this dimension:**
- Pattern-based anomaly detection beyond simple thresholds
- Proactive alert generation with root cause hypotheses
- Automated first-response actions (isolate, escalate, notify)
- Continuous monitoring with learning from feedback

### Collaboration (协调沟通)

**Core question:** Is cross-department, cross-role information alignment and status synchronization efficient?

**Judgment signals:**
- Significant time spent chasing progress, aligning information, and coordination meetings
- Project delays frequently trace back to information asymmetry
- Status updates require manual broadcasting across stakeholders
- Decision-making stalls waiting for input from multiple parties

**Typical scenarios:**
- Cross-department project coordination (sales ↔ production ↔ logistics)
- Customer communication and after-sales response coordination
- Vendor and supplier relationship management
- Internal approval chain acceleration
- Multi-stakeholder decision alignment

**Agent capabilities in this dimension:**
- Automated status aggregation and broadcast
- Intelligent meeting scheduling and preparation
- Action item tracking and deadline management
- Cross-team context bridging and translation

---

## Layer 3: Information Assetization

### Sedimentation (知识沉淀)

**Core question:** Can individual experience and tacit knowledge be solidified into organizational digital assets?

**Judgment signals:**
- Key personnel departure significantly impacts operations
- New employee onboarding cycles are excessively long
- Best practices are scattered across personal notes and individual experience
- Successful pilot projects cannot be replicated across teams

**Typical scenarios:**
- Expert decision logic capture and codification
- Knowledge base construction from fragmented sources
- Best practice documentation and maintenance
- Training material generation from operational data
- Post-mortem analysis and lesson-learned capture

**Agent capabilities in this dimension:**
- Decision trace logging with rationale capture
- Knowledge graph construction from operational records
- Automated documentation generation
- Experience pattern extraction and recommendation

### Scalability (规模弹性)

**Core question:** Can execution capacity scale beyond the physical limits of headcount?

**Judgment signals:**
- Peak periods heavily depend on overtime and temporary staff
- Linear headcount growth cannot keep pace with business growth
- Adding people does not proportionally increase throughput
- Quality degrades during high-volume periods

**Typical scenarios:**
- E-commerce peak season customer service scaling
- Month-end / quarter-end financial closing
- Regulatory reporting surge periods
- Product launch support scaling
- Batch processing of high-volume repetitive tasks

**Agent capabilities in this dimension:**
- Elastic capacity adjustment without proportional cost increase
- Parallel processing of independent tasks
- Quality-consistent execution at any volume
- Rapid onboarding of new task types through configuration

---

## Quick Reference: Dimension Selection Guide

| If user says... | Primary dimension | Secondary dimension |
|-----------------|-------------------|-------------------|
| "We spend all day entering data from emails and PDFs" | Perception | — |
| "We manually pull data from 5 systems to make decisions" | Analysis | Orchestration |
| "Every time something goes wrong, the whole process stops" | Orchestration | Monitoring |
| "We only find out about problems when customers complain" | Monitoring | Collaboration |
| "Half our time is spent in meetings chasing status" | Collaboration | Orchestration |
| "When our best person leaves, everything falls apart" | Sedimentation | Scalability |
| "We can't handle double the volume without doubling staff" | Scalability | Sedimentation |
| "Orders come from everywhere in every format" | Perception | Orchestration |
| "Sales, production, and logistics are always misaligned" | Collaboration | Monitoring |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)