docs(iit): Add IIT Manager Agent V2.9 development plan with multi-agent architecture
Features: - Add V2.9 enhancements: Cron Skill, User Profiling, Feedback Loop, Multi-Intent Handling - Create modular development plan documents (database, engines, services, memory, tasks) - Add V2.5/V2.6/V2.8/V2.9 design documents for architecture evolution - Add system design white papers and implementation guides Architecture: - Dual-Brain Architecture (SOP + ReAct engines) - Three-layer memory system (Flow Log, Hot Memory, History Book) - ProfilerService for personalized responses - SchedulerService with Cron Skill support Also includes: - Frontend nginx config updates - Backend test scripts for WeChat signature - Database backup files Co-authored-by: Cursor <cursoragent@cursor.com>
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# **IIT Manager Agent V2.2 落地实施指南：极简架构版 (融合 Moltbot)**
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**文档版本：** V2.2 (Moltbot Enhanced)
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**日期：** 2026-02-03
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**适用对象：** 2人高效开发团队
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**核心原则：** **Postgres-Only**, **No New Components**, **Service-First**, **Proactive & Personalized**
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**变更说明：** 在 V2.1 基础上，借鉴 Moltbot (Clawbot) 的主动性与记忆机制，增加了用户偏好存储、每日早报 Skill 和自我修正回路。
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## **1\. 核心架构：回归本质 (The Pragmatic Architecture)**
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我们将“Brain-Hand-Tool”模型映射到现有的 Node.js \+ Postgres 技术栈上。
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graph TD
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subgraph "Layer 3: 数字化大脑 (The Brain)"
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DB\[(Postgres DB)\]
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DB \--\>|加载 Skills & Preferences| Engine\[QcEngineService\]
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subgraph "混合双引擎"
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Engine \--\>|1. 执行 JSON Logic| Logic\[Engine A (硬规则)\]
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Engine \--\>|2. 组装 Prompt (含偏好)| LLM\[Engine B (软指令)\]
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end
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end
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subgraph "Layer 2: 伪 MCP 接口层 (The Hand)"
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LLM \--\>|3. 调用| Tools\[ToolsService (Class)\]
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Logic \--\>|3. 调用| Tools
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Tools \--\>|read\_data| Tool1\[RedcapAdapter\]
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Tools \--\>|search\_doc| Tool2\[VectorSearchService\]
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Tools \--\>|send\_msg| Tool3\[WechatService\]
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end
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subgraph "Layer 1: 基础设施 (The Tool)"
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Tool1 \--\> REDCap
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Tool2 \--\> pgvector
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Tool3 \--\> WeCom
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end
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## **2\. 数据层设计：Skills 即数据 (Skills as Data)**
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不再使用 OSS 存文件，直接使用 Postgres iit\_schema 存储结构化配置。**新增用户偏好表。**
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### **2.1 数据库 Schema**
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// schema.prisma
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// 核心技能表
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model IitSkill {
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id String @id @default(uuid())
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projectId String // 绑定项目 ID (如 PID=16)
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formName String // 绑定表单 (如 demographics) 或 场景 (如 daily\_briefing)
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// 核心配置 (JSON 类型)
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// 包含 hard\_rules, soft\_instructions, schedule (cron表达式)
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config Json
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isActive Boolean @default(true)
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version Int @default(1)
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updatedAt DateTime @updatedAt
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@@unique(\[projectId, formName\])
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@@map("iit\_skills")
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@@schema("iit\_schema")
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}
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// \[New\] 用户偏好表 (Moltbot 记忆机制)
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model IitUserPreference {
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id String @id @default(uuid())
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userId String // 绑定企业微信 UserID
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projectId String // 绑定项目
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// 偏好配置 (JSON)
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// 例如: { "report\_style": "concise", "notification\_time": "08:30" }
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preferences Json
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updatedAt DateTime @updatedAt
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@@unique(\[userId, projectId\])
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@@map("iit\_user\_preferences")
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@@schema("iit\_schema")
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}
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### **2.2 JSON 配置范例 (Skill)**
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**范例 1：质控 Skill**
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{
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"description": "人口学表单质控",
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"hard\_rules": \[
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{
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"field": "age",
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"logic": { "\>=": \[{ "var": "age" }, 18\] },
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"message": "年龄必须 \>= 18 岁"
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}
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\],
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"soft\_instructions": \[
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{
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"instruction": "检查备注字段。如果包含'无法签署知情同意'，请标记违规。",
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"tools": \["read\_clinical\_data"\]
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}
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\]
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}
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**范例 2：\[New\] 每日早报 Skill (Moltbot 主动性)**
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{
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"description": "每日项目进展早报",
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"schedule": "0 30 8 \* \* \*", // 每天 08:30 触发 (pg-boss)
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"soft\_instructions": \[
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{
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"instruction": "请统计昨天的入组人数、待处理质疑数。生成一段简报发给 PI。注意参考用户的 report\_style 偏好。",
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"tools": \["get\_project\_stats", "send\_wechat\_msg"\]
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}
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\]
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}
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## **3\. 服务层设计：伪 MCP 实现 (Service-First)**
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不要引入 @modelcontextprotocol/sdk。创建一个标准的 Service 类来管理工具。
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### **3.1 ToolsService.ts 实现规范**
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// backend/src/modules/iit-manager/services/ToolsService.ts
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import { RedcapAdapter } from '../adapters/RedcapAdapter';
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import { VectorSearchService } from '@/common/rag/vectorSearchService';
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// 定义工具接口 (为了给 LLM 生成菜单)
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export const TOOL\_DEFINITIONS \= \[
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{
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name: "read\_clinical\_data",
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description: "读取REDCap临床数据...",
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parameters: { ... }
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},
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{
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name: "update\_user\_preference", // \[New\] 更新偏好工具
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description: "更新用户的偏好设置，如报告风格、通知时间。",
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parameters: {
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type: "object",
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properties: {
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key: { type: "string", description: "偏好键名，如 report\_style" },
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value: { type: "string", description: "偏好值，如 concise (简洁) 或 detailed (详细)" }
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}
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}
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}
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\];
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export class ToolsService {
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constructor(
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private redcap: RedcapAdapter,
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private rag: VectorSearchService,
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private prisma: PrismaClient
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) {}
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// 统一执行入口 (LLM 只认识这个)
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async executeTool(name: string, args: any) {
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switch (name) {
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case 'read\_clinical\_data':
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return this.redcap.exportRecords(args);
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case 'update\_user\_preference':
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// \[New\] 实现记忆更新
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return this.updatePreference(args);
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case 'raise\_query':
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return this.savePendingAction(args);
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default:
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throw new Error(\`未知工具: ${name}\`);
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}
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}
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}
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## **4\. 业务层设计：混合引擎调度 (含自我修正)**
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### **4.1 执行逻辑 (The Pipeline)**
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// backend/src/modules/iit-manager/services/QcEngineService.ts
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export class QcEngineService {
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async runQualityCheck(projectId: string, formName: string, data: any, userId?: string) {
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// 1\. 加载 Skill 和 用户偏好
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const skill \= await prisma.iitSkill.findUnique({...});
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const userPref \= userId ? await prisma.iitUserPreference.findUnique({ where: { userId, projectId } }) : null;
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if (\!skill) return;
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// 2\. 运行 Engine A (硬规则)
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const engineA \= new HardRuleEngine();
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const hardErrors \= engineA.run(skill.config.hard\_rules, data);
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if (hardErrors.length \> 0\) {
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await this.saveShadowState(hardErrors);
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return;
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}
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// 3\. 运行 Engine B (软指令) \- \[New\] 注入偏好 Context
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const engineB \= new SoftRuleEngine(this.llm, this.toolsService);
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// 将偏好注入 Prompt: "用户偏好简洁的报告，请不要废话。"
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const context \= {
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...data,
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user\_preferences: userPref?.preferences
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};
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// \[New\] 开启自我修正回路 (Self-Correction Loop)
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const softSuggestions \= await engineB.runWithRetry(skill.config.soft\_instructions, context, 3);
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if (softSuggestions) {
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await this.saveShadowState(softSuggestions);
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}
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}
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}
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### **4.2 自我修正机制 (Self-Correction Loop)**
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借鉴 Moltbot，当工具调用失败时，不报错，而是让 LLM 重试。
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// SoftRuleEngine.ts (伪代码)
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async runWithRetry(instruction, context, maxRetries \= 3\) {
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let history \= \[...\];
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for (let i \= 0; i \< maxRetries; i++) {
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const response \= await llm.chat(history);
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if (response.hasToolCall) {
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try {
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const result \= await tools.executeTool(response.toolName, response.args);
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history.push({ role: 'tool', content: result });
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} catch (error) {
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// \[New\] 捕获错误，喂回给 LLM
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console.warn(\`Tool Error: ${error.message}\`);
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history.push({
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role: 'user',
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content: \`工具调用失败: ${error.message}。请检查参数并重试。\`
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});
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continue; // 让 LLM 重新思考
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}
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} else {
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return response.content;
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}
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}
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}
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## **5\. 实施路线图 (2人团队专属)**
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### **✅ Week 1: 基础建设 (Engine A)**
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1. 创建 iit\_skills 和 iit\_user\_preferences 表。
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2. 引入 json-logic-js 库。
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3. 实现 QcEngineService 的基础骨架，只跑硬规则。
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### **🚧 Week 2: AI 接入与主动性 (Engine B)**
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1. 创建 ToolsService，实现基础工具。
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2. 实现 SoftRuleEngine 的 **自我修正回路**。
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3. **里程碑**：配置 daily\_briefing Skill，利用 pg-boss 定时触发，早上 8:30 收到测试推送。
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### **📋 Week 3: 界面与优化**
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1. 开发 Admin 页面 (JSON Editor) 编辑 Skill。
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2. 完善影子状态 (Shadow State) 审核界面。
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## **6\. 多轮对话与开放式问答支持**
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### **6.1 核心策略：Intent Routing \+ General Skill**
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不要把所有逻辑都写死在质控里。我们增加一个特殊的 Skill：general\_qa。
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#### **A. 配置 general\_qa Skill**
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在数据库插入一条 formName \= 'general\_chat' 的记录：
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{
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"description": "通用问答与查询助手",
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"soft\_instructions": \[
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{
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"instruction": "你是本项目的智能助手。你可以回答关于项目进度、患者数据、方案细节的问题。请根据用户意图调用工具。",
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"tools": \["read\_clinical\_data", "search\_protocol", "get\_project\_stats", "update\_user\_preference"\]
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}
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\]
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}
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### **6.2 代码实现：聊天入口 (ChatService.ts)**
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// backend/src/modules/iit-manager/services/ChatService.ts
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export class ChatService {
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constructor(
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private qcEngine: QcEngineService,
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private sessionMemory: SessionMemory
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) {}
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async handleUserMessage(userId: string, projectId: string, message: string) {
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// 1\. 获取历史对话 & 用户偏好
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const history \= await this.sessionMemory.get(userId);
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const userPref \= await prisma.iitUserPreference.findUnique({...});
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// 2\. 加载通用 Skill
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const generalSkill \= await prisma.iitSkill.findUnique({
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where: { projectId, formName: 'general\_chat' }
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});
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// 3\. 构造 Prompt (包含 History \+ Skill \+ Preferences)
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const prompt \= \`
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${generalSkill.config.soft\_instructions\[0\].instruction}
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\[User Preferences\]: ${JSON.stringify(userPref?.preferences || {})}
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历史对话:
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${history.map(h \=\> \`${h.role}: ${h.content}\`).join('\\n')}
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用户当前问题: ${message}
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\`;
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// 4\. 调用 LLM (Engine B 逻辑复用，含自我修正)
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const response \= await this.qcEngine.runSoftAgent(prompt, generalSkill.config);
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// 5\. 更新记忆
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await this.sessionMemory.add(userId, { role: 'user', content: message });
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await this.sessionMemory.add(userId, { role: 'assistant', content: response });
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return response;
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}
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}
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## **7\. Moltbot 理念借鉴总结**
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| 借鉴点 | IIT Manager 落地实现 | 价值 |
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| :---- | :---- | :---- |
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| **主动性 (Proactivity)** | **每日早报 Skill**：利用 pg-boss 定时触发 daily\_briefing Skill，主动推送企微消息。 | 让用户感觉系统是活的，而不是被动的数据库。 |
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| **记忆 (Memory)** | **用户偏好表**：存储 report\_style, notification\_time 等，Prompt 动态注入。 | 提供千人千面的个性化体验。 |
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| **自我修正 (Self-Correction)** | **重试回路**：Engine B 在工具报错时，将错误信息回传给 LLM，允许其修正参数重试。 | 极大提升系统鲁棒性，减少人工运维。 |
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**结论：这就是 IIT Manager Agent 的最终形态。既有医疗的严谨（硬规则），又有个性化的温度（偏好记忆），还有极简的架构（Postgres-Only）。开干吧！**
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