commit ed616a8de95e19ce2c4ed87c734d23483f5a2deb Author: xiaohei Date: Sat Apr 11 17:49:30 2026 +0800 Initial commit: PRD v0.2, PROGRESS, knowledge base, PoC diff --git a/HEARTBEAT-CHECK.md b/HEARTBEAT-CHECK.md new file mode 100644 index 0000000..e99ab14 --- /dev/null +++ b/HEARTBEAT-CHECK.md @@ -0,0 +1,28 @@ +# FlowPilot 开发心跳检查配置 + +> 供OpenClaw cron使用，每30分钟检查一次项目进度 + +## 检查逻辑 + +1. 读 products/pmp-tool/PROGRESS.md +2. 找到"当前Issue"和"状态" +3. 如果状态是"进行中"： + - 检查上次更新时间 + - 如果超过30分钟未更新 → 检查Claude Code进程 + - 进程已死 → 根据断点信息重新启动 + - 进程还活着 → 不干预 +4. 如果状态是"已完成"但下一个Issue未启动 → 自动启动下一个 +5. 更新检查时间戳 + +## 恢复规则 + +- **代码未提交** → 先stash，然后从断点重新执行 +- **代码已提交但验证未通过** → 重新验证 +- **验证通过但未更新PROGRESS** → 更新PROGRESS，启动下一个Issue +- **连续3次失败** → 暂停，通知老板 + +## Cron配置建议 + +``` +openclaw cron add --schedule "*/30 * * * *" --task "读 products/pmp-tool/PROGRESS.md，检查项目进度。如果任务卡住超过30分钟，根据断点信息恢复。如果一切正常，回复 HEARTBEAT_OK。" +``` diff --git a/PRD.md b/PRD.md new file mode 100644 index 0000000..9d2a44a --- /dev/null +++ b/PRD.md @@ -0,0 +1,364 @@ +# FlowPilot - AI驱动的项目管理流程引擎 PRD + +> 创建时间：2026-04-11 +> 当前版本：v0.2 +> 状态：📋 规划中 + +## 1. 产品概述 + +**一句话：** AI Agent作为项目成员，基于PMBOK第8版自动执行项目管理全流程。 + +**目标用户：** 3-20人的软件开发团队，PM/技术负责人为主 + +**核心价值：** 用户创建项目后，系统自动引导走完启动→规划→执行→监控→收尾全流程。AI Agent按需动态创建、分配任务、执行交付、跟踪进度，关键节点与用户沟通决策。全过程记录可查。 + +**产品名：** FlowPilot（流程领航） + +## 2. 核心创新：双Agent管理架构 + +### 2.1 HR管理员（AI Agents HR Manager） + +**职责：** 根据项目需求，动态创建和管理执行任务的AI Agent + +**能力：** +- 分析项目阶段和任务需求，确定需要什么能力的Agent +- 选择合适的大模型（了解不同模型的能力边界） +- 编写和优化Agent的提示词（Prompt Engineering） +- 创建Agent实例并分配任务 +- 监控Agent执行过程，评分和质量把关 +- 根据执行反馈迭代优化提示词和模型选择 +- 终止或替换表现不佳的Agent + +**工作流程：** +``` +收到任务需求 + ↓ +判断需要什么能力（文档/分析/代码/协调） + ↓ +选择模型 + 编写提示词 + ↓ +创建Agent → 分配任务（原子粒度） + ↓ +监控执行 → 评分 + ├─ 通过 → 交付结果 + └─ 不通过 → 优化提示词 → 重试或换模型 +``` + +### 2.2 经验管理员（AI Agents Experience Manager） + +**职责：** 项目知识管理 + 跨Agent协调 + 全局把控 + +**能力：** +- 记录所有Agent的输入输出，形成项目知识库 +- 总结和归档项目执行经验 +- 为Agent补充项目上下文（解决单个Agent信息孤岛问题） +- 统筹跨Agent职能沟通和依赖协调 +- 从项目全局监控进度，识别瓶颈 +- 关键节点和遇到瓶颈时与用户沟通决策 +- 根据项目状态动态调整Agent任务和方向 + +**工作流程：** +``` +持续监听所有Agent的输入输出 + ↓ +记录并结构化存储 + ↓ +检测到：依赖缺失 → 补充上下文给相关Agent + 进度偏差 → 触发调整（通知HR管理员换Agent/改策略） + 关键决策点 → 暂停，推送给用户决策 + 瓶颈/异常 → 升级给用户 + ↓ +定期总结归档 → 更新项目知识库 +``` + +### 2.3 三方协作模型 + +``` + ┌─────────────┐ + │ 用户（老板） │ ← 关键决策点 + └──────┬──────┘ + │ 指令/确认 + ┌──────▼──────┐ + │ 经验管理员 │ ← 全局把控、知识管理 + └──────┬──────┘ + │ 任务分派/上下文补充 + ┌───────────┼───────────┐ + ▼ ▼ ▼ + Agent-A Agent-B Agent-C ← 执行层（动态创建/销毁） + (文档) (分析) (协调) + ▲ ▲ ▲ + └───────────┼───────────┘ + │ 创建/监控/评分/优化 + ┌──────┴──────┐ + │ HR管理员 │ ← Agent生命周期管理 + └─────────────┘ +``` + +## 3. 任务拆解引擎 + +### 3.1 核心难题：如何拆到单Agent单次可完成？ + +**答案：递归分解 + 原子任务类型库** + +### 3.2 原子任务的判定标准 + +一个任务可交给单Agent单次完成，必须满足： + +| 条件 | 含义 | +|------|------| +| **输入确定** | 所需信息完整，不需要追问 | +| **输出确定** | 有明确格式和验收标准 | +| **无外部依赖** | 不需要等别的Agent结果（或依赖已作为输入传入） | +| **时间可控** | 单次API调用在合理token内可完成 | + +### 3.3 原子任务类型库 + +预设一套标准化的任务类型，每种类型有固定的输入/输出Schema： + +**文档生成类（单次 2000-4000 token）：** +- `fill_template` — 给定模板+数据 → 输出填充后文档 +- `summarize` — 给定长文 → 输出摘要 +- `format_convert` — 给定内容 → 按目标格式输出 + +**分析判断类（单次 1000-2000 token）：** +- `evaluate` — 给定标准+对象 → 输出评分+理由 +- `risk_identify` — 给定范围 → 输出风险列表 +- `prioritize` — 给定列表+标准 → 输出排序结果 + +**协调沟通类（单次 1000-1500 token）：** +- `generate_notification` — 给定事件 → 输出消息内容 +- `aggregate_report` — 给定多条数据 → 输出汇总 + +**数据操作类（单次 1000-1500 token）：** +- `status_update` — 给定条件 → 查询并更新 +- `extract` — 给定源 → 提取指定字段 + +### 3.4 拆解流程 + +``` +用户创建项目 + ↓ +HR管理员分析项目 → 生成阶段任务清单 + ↓ +每个任务递归判断：是否满足原子任务标准？ + ├─ 满足 → 创建Agent执行（传入完整输入） + └─ 不满足 → 按原子任务类型库拆解为子任务列表 + ↓ + 子任务再判断（递归），直到全部原子化 + ↓ + 经验管理员记录拆解结果 → 生成任务依赖图 + ↓ + 按依赖顺序：无依赖的并行，有依赖的串行 + ↓ + HR管理员为每个原子任务创建Agent执行 +``` + +**原则：宁可拆细，不要贪大。** 一个任务失败只重试那一个，不连锁崩溃。 + +## 4. 功能列表 + +### P0 - 必须有（MVP，v0.2） + +| # | 功能 | 描述 | 状态 | +|---|------|------|------| +| P0-1 | 项目创建向导 | 引导用户完成项目章程（目标、范围、干系人、里程碑、资源），每步有说明和模板 | ⬜ | +| P0-2 | HR管理员 | 核心Agent管理器：分析任务→选模型→写提示词→创建Agent→监控评分→迭代优化 | ⬜ | +| P0-3 | 经验管理员 | 核心协调器：记录Agent输入输出→构建项目知识库→跨Agent协调→关键决策推送给用户 | ⬜ | +| P0-4 | 任务拆解引擎 | 递归分解+原子任务类型库，把复杂任务拆到单Agent单次可完成的粒度 | ⬜ | +| P0-5 | 看板视图 | 待办/进行中/已完成/待确认 四列看板，区分AI执行和人工任务 | ⬜ | +| P0-6 | 执行记录 | 全过程记录：每个Agent的输入、输出、评分、耗时，可查询可回溯 | ⬜ | +| P0-7 | 决策交互 | 关键节点推送决策给用户（飞书消息卡片），用户确认后AI继续执行 | ⬜ | +| P0-8 | 检查清单引擎 | 每个环节自动推送PMBOK检查清单，AI执行+人工确认 | ⬜ | + +### P1 - 应该有（v1.0） + +| # | 功能 | 描述 | 状态 | +|---|------|------|------| +| P1-1 | 干系人管理 | 权力-利益矩阵可视化，AI自动推荐参与策略 | ⬜ | +| P1-2 | WBS任务拆解 | 树形结构可视化，AI辅助拆解+人工调整 | ⬜ | +| P1-3 | 风险管理 | 风险登记册，AI自动识别风险+概率影响评估+应对策略建议 | ⬜ | +| P1-4 | 需求池管理 | MoSCoW优先级，用户故事模板，AI辅助需求分析和冲突检测 | ⬜ | +| P1-5 | 变更管理 | 变更请求→AI评估影响→用户审批→自动执行，全流程记录 | ⬜ | +| P1-6 | 项目健康度报告 | AI自动汇总生成周报，红黄绿灯+趋势分析 | ⬜ | +| P1-7 | 复盘与知识沉淀 | 项目收尾AI自动复盘，经验归入知识库，反哺未来项目 | ⬜ | +| P1-8 | 多模型支持 | HR管理员可根据任务类型选择不同模型（GPT/Claude/GLM/Qwen等） | ⬜ | + +### P2 - 可以有（v2.0+） + +| # | 功能 | 描述 | 状态 | +|---|------|------|------| +| P2-1 | RACI矩阵 | 任务-角色矩阵可视化，人+AI混合角色 | ⬜ | +| P2-2 | 里程碑时间线 | 甘特图/时间线视图，AI预测延误风险 | ⬜ | +| P2-3 | 多项目组合 | 跨项目状态总览，Agent资源调度 | ⬜ | +| P2-4 | 模板市场 | 不同行业的项目模板+Agent提示词模板 | ⬜ | +| P2-5 | 预算管理 | AI估算成本+跟踪实际+超支预警 | ⬜ | +| P2-6 | Agent能力市场 | 社区共享优化好的Agent提示词配置 | ⬜ | +| P2-7 | 挣值分析 | PV/EV/AC自动计算，SPI/CPI图表 | ⬜ | + +## 5. 技术方案 + +### 5.1 平台 + +**飞书应用（Web App/H5）** + +飞书作为用户入口和消息通道，FlowPilot后端独立部署。 + +### 5.2 技术栈 + +``` +前端：React 18 + TypeScript + Arco Design +后端：Node.js + Hono +数据库：PostgreSQL（主库）+ Redis（缓存/队列/会话） +AI编排层：自研轻量Agent框架 + - Agent生命周期管理 + - 任务拆解引擎 + - 多模型路由（OpenAI/Claude/GLM/Qwen API） + - 提示词版本管理 +消息通道：飞书开放平台 SDK +部署：Docker Compose（初期单机）→ K8s（规模化后） +``` + +### 5.3 核心架构 + +``` +┌──────────────────────────────────────────────────┐ +│ 飞书客户端 │ +│ （项目管理界面 / 消息卡片 / 通知） │ +└──────────────────┬───────────────────────────────┘ + │ 飞书开放平台 +┌──────────────────▼───────────────────────────────┐ +│ FlowPilot 后端 │ +│ ┌────────────┐ ┌─────────────┐ ┌───────────┐ │ +│ │ HR管理员 │ │ 经验管理员 │ │ 任务拆解 │ │ +│ │ Agent │ │ Agent │ │ 引擎 │ │ +│ └─────┬──────┘ └──────┬──────┘ └─────┬─────┘ │ +│ │ │ │ │ +│ ┌─────▼────────────────▼───────────────▼─────┐ │ +│ │ Agent 编排层 │ │ +│ │ - 生命周期管理 - 多模型路由 - 提示词管理 │ │ +│ │ - 执行记录 - 依赖调度 - 失败重试 │ │ +│ └──────────────────┬────────────────────────┘ │ +│ │ │ +│ ┌──────────────────▼────────────────────────┐ │ +│ │ 执行Agent池（动态创建/销毁） │ │ +│ │ Agent-1(doc) Agent-2(analysis) ... │ │ +│ └───────────────────────────────────────────┘ │ +└──────────────────┬───────────────────────────────┘ + │ +┌──────────────────▼───────────────────────────────┐ +│ PostgreSQL + Redis │ +│ - 项目数据 - Agent记录 - 知识库 - 提示词版本 │ +└──────────────────────────────────────────────────┘ +``` + +### 5.4 数据模型 + +``` +Project（项目） +├── Charter（章程） +├── Stakeholder[]（干系人） +├── Milestone[]（里程碑） +├── Phase[]（阶段：启动/规划/执行/监控/收尾） +│ └── TaskNode[]（任务树，递归拆解后的原子任务） +│ ├── agent_id（执行Agent） +│ ├── agent_config（模型+提示词版本） +│ ├── input（完整输入） +│ ├── output（执行输出） +│ ├── score（HR管理员评分） +│ └── status（待分配/执行中/已完成/待确认/失败） +├── Risk[]（风险） +├── ChangeRequest[]（变更请求） +├── KnowledgeEntry[]（项目知识库） +├── HealthReport[]（健康度报告） +└── ExecutionLog[]（全量执行记录） + +AgentConfig（Agent配置库） +├── agent_type（原子任务类型） +├── model（大模型标识） +├── prompt_version（提示词版本） +├── prompt_template（提示词模板） +├── avg_score（历史平均评分） +└── usage_count（使用次数） + +DecisionLog（决策记录） +├── task_id（关联任务） +├── decision_type（AI自主/人工确认/人工决策） +├── context（决策上下文） +├── decision（决策结果） +├── decider（user / hr_manager / experience_manager） +└── timestamp +``` + +## 6. 里程碑 + +| 版本 | 目标 | 预计完成 | 实际完成 | 状态 | +|------|------|---------|---------|------| +| v0.1 | PRD初稿 | 2026-04-13 | — | ✅ | +| v0.2 | PRD升级版（AI Agent架构） | 2026-04-15 | — | 🔨 | +| v0.3 | 技术验证：Agent编排原型 | 2026-05-15 | — | ⬜ | +| v0.5 | MVP内测（P0功能，3个种子项目） | 2026-06-30 | — | ⬜ | +| v1.0 | 正式版（P0+P1） | 2026-08-31 | — | ⬜ | +| v2.0 | 高级功能（P2+Agent市场） | 2026-Q4 | — | ⬜ | + +## 7. 商业化策略 + +**核心卖点：AI Agent团队，不只是AI辅助** + +**定价：** +- 免费版：1个项目，基础流程引导，AI辅助文档生成 +- 专业版：¥199/月/团队，完整Agent团队+知识库+全流程自动化 +- 企业版：¥499/月，多项目+自定义Agent+私有部署 + +**为什么能收费：** 用户买的是一个**AI项目团队**，不是工具。相当于用1/10的人力成本获得一个标准化的项目管理团队。 + +**获客路径：** +1. 种子用户：CIO协会人脉免费试用，收集案例 +2. 飞书应用市场（"AI项目管理"是热门搜索词） +3. 内容营销：AI Agent做项目管理的实战案例 +4. 口碑：推荐奖励+Agent模板分享 + +## 8. 关键技术挑战与应对 + +| 挑战 | 难度 | 应对策略 | +|------|------|---------| +| 任务拆解粒度 | ⭐⭐⭐ | 原子任务类型库 + 递归分解，宁可拆细 | +| 多Agent上下文共享 | ⭐⭐⭐⭐ | 经验管理员统一管理知识库，Agent间不直接通信 | +| 提示词工程自动化 | ⭐⭐⭐⭐ | HR管理员评分驱动迭代，积累优质提示词库 | +| 单次任务失败重试 | ⭐⭐ | 自动重试+换模型+人工兜底，三层保障 | +| AI执行成本控制 | ⭐⭐⭐ | 小模型做常规任务，大模型做复杂任务，分级路由 | +| 飞书平台审核 | ⭐⭐ | 研究审核规范，预留调整时间 | + +## 9. 差异化定位 + +| 维度 | 传统PM工具 | AI辅助PM | **FlowPilot** | +|------|-----------|---------|-------------| +| AI角色 | 无 | 写作助手/聊天机器人 | **项目团队成员** | +| 流程执行 | 人驱动 | 人驱动+AI建议 | **AI执行+人决策** | +| Agent管理 | 无 | 固定助手 | **HR管理员动态创建优化** | +| 知识积累 | 手动归档 | 手动归档 | **经验管理员自动沉淀** | +| 团队构成 | 纯人类 | 人类+工具 | **人类+AI Agents混合** | + +## 10. 迭代日志 + +### 2026-04-11 - PRD v0.2 升级 +- 引入双Agent管理架构（HR管理员+经验管理员） +- 新增任务拆解引擎设计（递归分解+原子任务类型库） +- 功能列表按AI Agent能力重新组织 +- 技术架构升级为多Agent编排层 +- 新增数据模型：AgentConfig、DecisionLog +- 商业化策略调整为"AI团队"而非"AI工具" +- 里程碑重新规划 + +### 2026-04-11 - PRD v0.1 初稿 +- 基于项目管理执行手册生成PRD +- 确定飞书应用为MVP平台 +- 定义P0/P1/P2功能优先级 + +## 11. 待讨论 / 风险 + +- [ ] 技术验证：先做一个Agent编排的PoC，验证可行性 +- [ ] 多模型API成本测算：一个完整项目跑下来大概多少API费用？ +- [ ] 飞书应用审核：AI生成内容的审核要求 +- [ ] 竞品动态：Microsoft Copilot在项目管理领域的进展 +- [ ] 用户信任：如何让用户信任AI的执行结果？评分机制是否足够？ +- [ ] 提示词安全：如何防止Agent被注入攻击？ diff --git a/PROGRESS.md b/PROGRESS.md new file mode 100644 index 0000000..70feadd --- /dev/null +++ b/PROGRESS.md @@ -0,0 +1,53 @@ +# FlowPilot 开发进度追踪 + +> 本文件是项目连续性的核心保障。任何新会话启动时，先读本文件。 + +## 当前状态 + +- **当前Issue:** 准备创建Gitea仓库并导入第一批Issues +- **状态:** 📋 规划完成，待启动开发 +- **上次更新:** 2026-04-11 17:30 +- **PRD版本:** v0.2 + +## Issue流水线 + +| # | Issue | 描述 | 状态 | 断点备注 | +|---|-------|------|------|---------| +| 0 | 项目初始化 | 创建Gitea仓库、目录结构、基础配置 | 🔨 进行中 | 待创建仓库 | +| 1 | 项目创建向导页 | React+Arco Design向导框架 | ⬜ | | +| 2 | 章程模板填充 | 基于用户输入生成章程Markdown | ⬜ | | +| 3 | 飞书消息发送 | Node.js+Hono调用飞书Open API | ⬜ | | +| 4 | 看板基础组件 | 三列看板+拖拽(dnd-kit) | ⬜ | | +| 5 | 任务数据模型 | PostgreSQL建表+CRUD | ⬜ | | +| 6 | HR管理员原型 | 任务拆解逻辑 | ⬜ | | +| 7 | 经验管理员原型 | 执行记录存储和查询 | ⬜ | | +| 8 | 决策交互卡片 | 飞书消息卡片+回调 | ⬜ | | +| 9 | 检查清单引擎 | PMBOK检查清单展示+勾选 | ⬜ | | +| 10 | 执行记录API | REST接口查询Agent日志 | ⬜ | | +| 11 | 创建流程串联 | 向导→章程→任务→看板→通知全流程 | ⬜ | | +| 12 | 基础CI/CD | GitHub Actions lint+单测 | ⬜ | | +| 13 | 内测反馈表单 | 飞书表单收集用户反馈 | ⬜ | | +| 14 | 打包部署脚本 | Docker-compose一键启动 | ⬜ | | + +## 断点续传信息 + +（每次任务中断时更新此区域，新会话从这里恢复） + +``` +当前任务: 项目初始化 +Claude Code执行状态: 未启动 +已交付文件: poc/agent_poc.py, poc/README.md, PRD.md +Git状态: poc目录已init，未push +下一步: 创建Gitea仓库 → 导入Issues → 启动Issue #1 +``` + +## 里程碑 + +| 里程碑 | 目标日期 | 状态 | +|--------|---------|------| +| 仓库建立+Issue导入 | 2026-04-12 | 🔨 | +| 前端框架+基础页面 | 2026-04-25 | ⬜ | +| 后端API+数据库 | 2026-05-10 | ⬜ | +| Agent编排核心 | 2026-05-25 | ⬜ | +| 全流程串联 | 2026-06-10 | ⬜ | +| 内测版发布 | 2026-06-30 | ⬜ | diff --git a/knowledge/DEV-KNOWLEDGE.md b/knowledge/DEV-KNOWLEDGE.md new file mode 100644 index 0000000..2222754 --- /dev/null +++ b/knowledge/DEV-KNOWLEDGE.md @@ -0,0 +1,63 @@ +# FlowPilot 开发知识库 + +> 记录开发过程中的经验、有效的Prompt模式、遇到的坑。供后续任务复用。 + +## Prompt模式库 + +### 模式1：React组件开发 +``` +在 src/components/ 目录下创建 [组件名] 组件。 +技术栈：React 18 + TypeScript + Arco Design +要求： +1. 使用函数式组件 + Hooks +2. Props接口明确定义 +3. 包含基本样式 +4. 导出组件 + +参考已有组件风格：[列出已有文件] +``` + +### 模式2：后端API开发 +``` +在 src/routes/ 目录下创建 [路由名] 路由。 +框架：Hono (Node.js) +要求： +1. RESTful风格 +2. 输入验证 +3. 错误处理 +4. TypeScript类型 + +数据库操作参考：src/db/schema.ts +``` + +### 模式3：飞书集成 +``` +实现飞书 [功能] 集成。 +SDK：@larksuiteoapi/node-sdk +参考文档：[链接] +要求： +1. 使用已有飞书客户端配置（src/config/feishu.ts） +2. 错误处理和重试 +3. 日志记录 +``` + +## 经验记录 + +### 2026-04-11 - PoC阶段 +- **PoC验证通过**：HR管理员+经验管理员+任务拆解的核心链路可行 +- **Python PoC**：用模拟执行验证了架构，实际开发用Node.js +- **Claude Code调用**：使用 `--print --permission-mode bypassPermissions` 模式 +- **Codex不可用**：本机未安装codex CLI，改用claude code + +## 技术决策记录 + +| 决策 | 原因 | 日期 | +|------|------|------| +| Node.js后端 | 与飞书SDK生态一致，团队技能匹配 | 2026-04-11 | +| Arco Design | 字节出品，与飞书UI风格统一 | 2026-04-11 | +| PostgreSQL | 关系型数据适合项目管理结构化数据 | 2026-04-11 | +| 先串行后并行 | Agent编排初期用串行降低复杂度 | 2026-04-11 | + +## 遇到的坑 + +（开发过程中持续更新） diff --git a/poc/.gitignore b/poc/.gitignore new file mode 100644 index 0000000..aa0926a --- /dev/null +++ b/poc/.gitignore @@ -0,0 +1,4 @@ +node_modules/ +dist/ +.env +*.log diff --git a/poc/README.md b/poc/README.md new file mode 100644 index 0000000..a20c1f2 --- /dev/null +++ b/poc/README.md @@ -0,0 +1,66 @@ +# FlowPilot Agent Orchestration PoC + +This proof-of-concept demonstrates the core agent orchestration ideas from the FlowPilot PRD: + +## Core Concepts Demonstrated + +1. **HR Manager Agent**: Decomposes high-level tasks into atomic tasks and creates executor agents +2. **Experience Manager Agent**: Logs executions, provides context, and maintains a knowledge base +3. **Atomic Task Decomposition**: Breaking down complex tasks into single-agent-completable units +4. **Agent Lifecycle Management**: Dynamic creation of executor agents for specific task types + +## How to Run + +```bash +python3 agent_poc.py +``` + +## Output + +The PoC processes three sample high-level tasks from the project management handbook: +- Creating a project charter +- Identifying software development risks +- Creating a project schedule + +For each task, it shows: +- How the HR Manager decomposes the task into atomic tasks +- How executor agents are created for each atomic task type +- How the Experience Manager logs executions and provides context +- The final results and execution logs + +## Next Steps for Development + +1. **Replace simulation with actual LLM calls** - Integrate with OpenAI/Anthropic/etc APIs +2. **Add real task execution** - Instead of mock results, have agents actually perform tasks +3. **Implement persistence** - Store agent configurations, execution logs, and knowledge base in database +4. **Add feedback loops** - HR Manager improves based on execution scores +5. **Integrate with Feishu** - Build the actual frontend and Feishu bot interfaces + +## Files + +- `agent_poc.py`: Main PoC implementation +- `poc_results.json`: Detailed execution results from the last run +- `.gitignore`: Git ignore file + +## Design Notes + +This PoC focuses on demonstrating the orchestration logic rather than actual AI execution. +In a real implementation: +- The `_simulate_execution` method would call actual LLM APIs +- Agents would have specific prompt templates for their task types +- The Experience Manager would use vector embeddings for context retrieval +- Task decomposition would be more sophisticated (possibly using LLMs themselves) + +## Relation to PRD + +This PoC validates the core architectural concepts from PRD v0.2: +- Dual agent management (HR Manager + Experience Manager) +- Atomic task decomposition +- Agent lifecycle management +- Context sharing and knowledge base + +It does not yet cover: +- Feishu integration +- Full PMBOK process coverage +- Production-level error handling and scaling +- Actual LLM provider integration \ No newline at end of file diff --git a/poc/agent_poc.py b/poc/agent_poc.py new file mode 100644 index 0000000..80cc780 --- /dev/null +++ b/poc/agent_poc.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 +""" +Agent Orchestration PoC for FlowPilot +Demonstrates: +- HR Manager creating agents for tasks +- Experience Manager logging and context sharing +- Task decomposition into atomic types +""" + +import uuid +from datetime import datetime +from enum import Enum +from typing import Dict, List, Any, Optional + + +class TaskType(Enum): + DOCUMENT_GENERATION = "document_generation" + ANALYSIS = "analysis" + COORDINATION = "coordination" + DATA_OPERATION = "data_operation" + + +class AgentRole(Enum): + HR_MANAGER = "hr_manager" + EXPERIENCE_MANAGER = "experience_manager" + EXECUTOR = "executor" + + +class TaskStatus(Enum): + PENDING = "pending" + ASSIGNED = "assigned" + IN_PROGRESS = "in_progress" + COMPLETED = "completed" + FAILED = "failed" + + +class AtomicTask: + """Represents a task that can be completed by a single agent in one session.""" + + def __init__(self, + task_type: TaskType, + description: str, + input_data: Dict[str, Any], + expected_output_schema: Dict[str, Any]): + self.id = str(uuid.uuid4()) + self.task_type = task_type + self.description = description + self.input_data = input_data + self.expected_output_schema = expected_output_schema + self.status = TaskStatus.PENDING + self.agent_id: Optional[str] = None + self.result: Optional[Dict[str, Any]] = None + self.created_at = datetime.now() + self.completed_at: Optional[datetime] = None + + def to_dict(self): + return { + "id": self.id, + "task_type": self.task_type.value, + "description": self.description, + "input_data": self.input_data, + "expected_output_schema": self.expected_output_schema, + "status": self.status.value, + "agent_id": self.agent_id, + "result": self.result, + "created_at": self.created_at.isoformat(), + "completed_at": self.completed_at.isoformat() if self.completed_at else None + } + + +class Agent: + """Base agent class.""" + + def __init__(self, agent_id: str, role: AgentRole, model: str = "default"): + self.agent_id = agent_id + self.role = role + self.model = model + self.capabilities: List[TaskType] = [] + + def execute(self, task: AtomicTask) -> Dict[str, Any]: + """Execute a task and return result.""" + raise NotImplementedError + + def can_handle(self, task_type: TaskType) -> bool: + return task_type in self.capabilities + + +class HRManagerAgent(Agent): + """HR Manager: creates and manages executor agents.""" + + def __init__(self, agent_id: str): + super().__init__(agent_id, AgentRole.HR_MANAGER) + # HR manager can handle task decomposition and agent creation + self.capabilities = [TaskType.DOCUMENT_GENERATION, TaskType.ANALYSIS] + self.available_models = { + TaskType.DOCUMENT_GENERATION: "claude-3-sonnet", + TaskType.ANALYSIS: "gpt-4-turbo", + TaskType.COORDINATION: "claude-3-haiku", + TaskType.DATA_OPERATION: "gpt-3.5-turbo" + } + + def decompose_task(self, high_level_task: str) -> List[AtomicTask]: + """Decompose a high-level task into atomic tasks. + In PoC, we'll use simple heuristics. + """ + # Simple decomposition logic for demonstration + if "章程" in high_level_task or "charter" in high_level_task.lower(): + return [ + AtomicTask( + task_type=TaskType.DOCUMENT_GENERATION, + description="Generate project charter based on user input", + input_data={"template": "project_charter", "user_input": high_level_task}, + expected_output_schema={"sections": ["目标", "范围", "里程碑", "干系人"]} + ), + AtomicTask( + task_type=TaskType.ANALYSIS, + description="Analyze stakeholders and create stakeholder register", + input_data={"charter_placeholder": "will be filled"}, + expected_output_schema={"stakeholders": [{"name": "", "role": "", "power": 0, "interest": 0}]} + ) + ] + elif "风险" in high_level_task or "risk" in high_level_task.lower(): + return [ + AtomicTask( + task_type=TaskType.ANALYSIS, + description="Identify project risks", + input_data={"scope": high_level_task}, + expected_output_schema={"risks": [{"description": "", "probability": 0, "impact": 0}]} + ), + AtomicTask( + task_type=TaskType.DOCUMENT_GENERATION, + description="Generate risk mitigation plan", + input_data={"risks_placeholder": "will be filled"}, + expected_output_schema={"mitigations": []} + ) + ] + else: + # Default: treat as documentation task + return [ + AtomicTask( + task_type=TaskType.DOCUMENT_GENERATION, + description=f"Generate document for: {high_level_task}", + input_data={"request": high_level_task}, + expected_output_schema={"content": ""} + ) + ] + + def create_executor_agent(self, task_type: TaskType) -> Agent: + """Create an executor agent for a specific task type.""" + agent_id = str(uuid.uuid4()) + # In reality, this would configure the agent with specific prompts and model + executor = Agent(agent_id, AgentRole.EXECUTOR, + model=self.available_models.get(task_type, "default")) + executor.capabilities = [task_type] + return executor + + def execute(self, task: AtomicTask) -> Dict[str, Any]: + """HR Manager handles task decomposition and agent creation.""" + # For PoC, we simulate the decomposition + atomic_tasks = self.decompose_task(task.description) + task.agent_id = self.agent_id + task.status = TaskStatus.COMPLETED + task.result = { + "atomic_tasks": [at.to_dict() for at in atomic_tasks] + } + return task.result + + +class ExperienceManagerAgent(Agent): + """Experience Manager: logs executions, provides context, manages knowledge.""" + + def __init__(self, agent_id: str): + super().__init__(agent_id, AgentRole.EXPERIENCE_MANAGER) + self.capabilities = [TaskType.DATA_OPERATION, TaskType.ANALYSIS] + self.execution_log: List[Dict] = [] + self.knowledge_base: Dict[str, Any] = {} + + def record_execution(self, agent_id: str, task: AtomicTask, result: Dict[str, Any]): + """Record an agent's task execution.""" + log_entry = { + "timestamp": datetime.now().isoformat(), + "agent_id": agent_id, + "task_id": task.id, + "task_type": task.task_type.value, + "input": task.input_data, + "output": result, + "status": task.status.value + } + self.execution_log.append(log_entry) + + # Update knowledge base with patterns + self._update_knowledge_base(task, result) + + def _update_knowledge_base(self, task: AtomicTask, result: Dict[str, Any]): + """Simple knowledge base update: store successful patterns.""" + if task.status == TaskStatus.COMPLETED: + key = f"{task.task_type.value}_{hash(task.description) % 1000}" + self.knowledge_base[key] = { + "task_description": task.description, + "successful_input_pattern": task.input_data, + "successful_output_pattern": result, + "count": self.knowledge_base.get(key, {}).get("count", 0) + 1 + } + + def get_context_for_task(self, task_type: TaskType, keywords: str = "") -> Dict[str, Any]: + """Retrieve relevant context for a task.""" + # Simple context retrieval: return recent similar executions + relevant_logs = [ + log for log in self.execution_log[-20:] # last 20 + if log["task_type"] == task_type.value + ] + + return { + "recent_executions": relevant_logs, + "knowledge_base_snapshot": dict(list(self.knowledge_base.items())[:5]) # first 5 + } + + def execute(self, task: AtomicTask) -> Dict[str, Any]: + """Experience Manager handles data operations and context queries.""" + # For PoC, we simulate context provision + context = self.get_context_for_task(task.task_type, + str(task.input_data)) + task.agent_id = self.agent_id + task.status = TaskStatus.COMPLETED + task.result = { + "context": context, + "message": "Context provided for task execution" + } + return task.result + + +class TaskOrchestrator: + """Orchestrates the workflow between HR Manager, Experience Manager, and executors.""" + + def __init__(self): + self.hr_manager = HRManagerAgent("hr-001") + self.experience_manager = ExperienceManagerAgent("exp-001") + self.executor_agents: Dict[str, Agent] = {} + self.tasks: Dict[str, AtomicTask] = {} + + def process_high_level_task(self, description: str) -> Dict[str, Any]: + """Process a high-level task through the orchestrator.""" + print(f"\n=== Processing High-Level Task: {description} ===") + + # Step 1: HR Manager decomposes the task + hr_task = AtomicTask( + task_type=TaskType.ANALYSIS, # HR uses analysis to decompose + description=description, + input_data={}, + expected_output_schema={"atomic_tasks": []} + ) + + decomposition_result = self.hr_manager.execute(hr_task) + self.experience_manager.record_execution(self.hr_manager.agent_id, hr_task, decomposition_result) + + atomic_tasks_data = decomposition_result.get("atomic_tasks", []) + atomic_tasks = [] + + # Reconstruct AtomicTask objects from decomposition result + for task_data in atomic_tasks_data: + task = AtomicTask( + task_type=TaskType(task_data["task_type"]), + description=task_data["description"], + input_data=task_data["input_data"], + expected_output_schema=task_data["expected_output_schema"] + ) + task.id = task_data["id"] + atomic_tasks.append(task) + self.tasks[task.id] = task + + print(f"HR Manager decomposed into {len(atomic_tasks)} atomic tasks") + + # Step 2: For each atomic task, create/execute agent + results = [] + for task in atomic_tasks: + print(f"\n--- Executing Task: {task.description} ---") + + # Get context from Experience Manager + context_task = AtomicTask( + task_type=TaskType.DATA_OPERATION, + description="Get context for task execution", + input_data={"task_type": task.task_type.value, "keywords": task.description}, + expected_output_schema={"context": {}} + ) + context_result = self.experience_manager.execute(context_task) + self.experience_manager.record_execution(self.experience_manager.agent_id, context_task, context_result) + + # Create executor agent for this task type + executor_id = str(uuid.uuid4()) + executor = self.hr_manager.create_executor_agent(task.task_type) + executor.agent_id = executor_id # Override with generated ID + self.executor_agents[executor_id] = executor + + # Simulate task execution (in PoC, we just generate a mock result) + # In reality, this would call the actual agent/LLM + mock_result = self._simulate_execution(executor, task, context_result["context"]) + + # Record execution + task.agent_id = executor_id + task.status = TaskStatus.COMPLETED + task.result = mock_result + task.completed_at = datetime.now() + + self.experience_manager.record_execution(executor_id, task, mock_result) + results.append({ + "task": task.to_dict(), + "executor_id": executor_id, + "result": mock_result + }) + + print(f"Task completed by executor {executor_id[:8]}") + + return { + "high_level_task": description, + "hr_manager_task": hr_task.to_dict(), + "atomic_tasks": [t.to_dict() for t in atomic_tasks], + "execution_results": results, + "experience_manager_log": self.experience_manager.execution_log[-len(atomic_tasks)*2:] # recent logs + } + + def _simulate_execution(self, executor: Agent, task: AtomicTask, context: Dict) -> Dict[str, Any]: + """Simulate agent execution. In real system, this would call LLM.""" + # Simple mock based on task type + if task.task_type == TaskType.DOCUMENT_GENERATION: + return { + "generated_document": f"# Sample Document\n\nThis is a generated document for: {task.description}\n\nSections: {list(task.expected_output_schema.keys())}", + "model_used": executor.model, + "tokens_used": 1500 + } + elif task.task_type == TaskType.ANALYSIS: + return { + "analysis_result": f"Analysis of: {task.description}\n\nKey findings: \n- Item 1: ...\n- Item 2: ...\n- Recommendation: ...", + "model_used": executor.model, + "tokens_used": 800 + } + else: + return { + "output": f"Executed {task.task_type.value} for: {task.description}", + "model_used": executor.model, + "tokens_used": 500 + } + + +def main(): + """Run the PoC demonstration.""" + print("FlowPilot Agent Orchestration PoC") + print("=" * 50) + + orchestrator = TaskOrchestrator() + + # Example high-level tasks from the project management handbook + tasks_to_process = [ + "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线", + "识别软件开发项目的主要风险", + "制定项目进度计划" + ] + + all_results = [] + for task_desc in tasks_to_process: + result = orchestrator.process_high_level_task(task_desc) + all_results.append(result) + print("\n" + "="*50) + + # Summary + print("\n=== PoC Execution Summary ===") + print(f"High-level tasks processed: len {len(all_results)}") + total_atomic = sum(len(r["atomic_tasks"]) for r in all_results) + print(f"Total atomic tasks executed: {total_atomic}") + print(f"HR Manager agent ID: {orchestrator.hr_manager.agent_id}") + print(f"Experience Manager agent ID: {orchestrator.experience_manager.agent_id}") + print(f"Executor agents created: {len(orchestrator.executor_agents)}") + + # Save results to file for inspection + import json + with open("poc_results.json", "w") as f: + json.dump(all_results, f, indent=2, ensure_ascii=False) + print("\nDetailed results saved to poc_results.json") + + +if __name__ == "__main__": + main() diff --git a/poc/poc_results.json b/poc/poc_results.json new file mode 100644 index 0000000..90ebc2f --- /dev/null +++ b/poc/poc_results.json @@ -0,0 +1,8028 @@ +[ + { + "high_level_task": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线", + "hr_manager_task": { + "id": "f62bc78a-c71e-4df8-823f-042859ecaa84", + "task_type": "analysis", + "description": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线", + "input_data": {}, + "expected_output_schema": { + "atomic_tasks": [] + }, + "status": "completed", + "agent_id": "hr-001", + "result": { + "atomic_tasks": [ + { + "id": "4ff13ffa-980e-48ff-bce6-163b589e69b1", + "task_type": "document_generation", + "description": "Generate project charter based on user input", + "input_data": { + "template": "project_charter", + "user_input": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线" + }, + "expected_output_schema": { + "sections": [ + "目标", + "范围", + "里程碑", + "干系人" + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404439", + "completed_at": null + }, + { + "id": "d582bdbe-47e4-41db-9ae9-98e4fc1b893b", + "task_type": "analysis", + "description": "Analyze stakeholders and create stakeholder register", + "input_data": { + "charter_placeholder": "will be filled" + }, + "expected_output_schema": { + "stakeholders": [ + { + "name": "", + "role": "", + "power": 0, + "interest": 0 + } + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404475", + "completed_at": null + } + ] + }, + "created_at": "2026-04-11T16:51:20.403795", + "completed_at": null + }, + "atomic_tasks": [ + { + "id": "4ff13ffa-980e-48ff-bce6-163b589e69b1", + "task_type": "document_generation", + "description": "Generate project charter based on user input", + "input_data": { + "template": "project_charter", + "user_input": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线" + }, + "expected_output_schema": { + "sections": [ + "目标", + "范围", + "里程碑", + "干系人" + ] + }, + "status": "completed", + "agent_id": "116c77a3-f8ac-4c14-9e12-e1e7098898c3", + "result": { + "generated_document": "# Sample Document\n\nThis is a generated document for: Generate project charter based on user input\n\nSections: ['sections']", + "model_used": "claude-3-sonnet", + "tokens_used": 1500 + }, + "created_at": "2026-04-11T16:51:20.404611", + "completed_at": "2026-04-11T16:51:20.404796" + }, + { + "id": "d582bdbe-47e4-41db-9ae9-98e4fc1b893b", + "task_type": "analysis", + "description": "Analyze stakeholders and create stakeholder register", + "input_data": { + "charter_placeholder": "will be filled" + }, + "expected_output_schema": { + "stakeholders": [ + { + "name": "", + "role": "", + "power": 0, + "interest": 0 + } + ] + }, + "status": "completed", + "agent_id": "565c7f6d-e2af-4eef-a037-49a66ea9fda7", + "result": { + "analysis_result": "Analysis of: Analyze stakeholders and create stakeholder register\n\nKey findings: \n- Item 1: ...\n- Item 2: ...\n- Recommendation: ...", + "model_used": "gpt-4-turbo", + "tokens_used": 800 + }, + "created_at": "2026-04-11T16:51:20.404640", + "completed_at": "2026-04-11T16:51:20.404960" + } + ], + "execution_results": [ + { + "task": { + "id": "4ff13ffa-980e-48ff-bce6-163b589e69b1", + "task_type": "document_generation", + "description": "Generate project charter based on user input", + "input_data": { + "template": "project_charter", + "user_input": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线" + }, + "expected_output_schema": { + "sections": [ + "目标", + "范围", + "里程碑", + "干系人" + ] + }, + "status": "completed", + "agent_id": "116c77a3-f8ac-4c14-9e12-e1e7098898c3", + "result": { + "generated_document": "# Sample Document\n\nThis is a generated document for: Generate project charter based on user input\n\nSections: ['sections']", + "model_used": "claude-3-sonnet", + "tokens_used": 1500 + }, + "created_at": "2026-04-11T16:51:20.404611", + "completed_at": "2026-04-11T16:51:20.404796" + }, + "executor_id": "116c77a3-f8ac-4c14-9e12-e1e7098898c3", + "result": { + "generated_document": "# Sample Document\n\nThis is a generated document for: Generate project charter based on user input\n\nSections: ['sections']", + "model_used": "claude-3-sonnet", + "tokens_used": 1500 + } + }, + { + "task": { + "id": "d582bdbe-47e4-41db-9ae9-98e4fc1b893b", + "task_type": "analysis", + "description": "Analyze stakeholders and create stakeholder register", + "input_data": { + "charter_placeholder": "will be filled" + }, + "expected_output_schema": { + "stakeholders": [ + { + "name": "", + "role": "", + "power": 0, + "interest": 0 + } + ] + }, + "status": "completed", + "agent_id": "565c7f6d-e2af-4eef-a037-49a66ea9fda7", + "result": { + "analysis_result": "Analysis of: Analyze stakeholders and create stakeholder register\n\nKey findings: \n- Item 1: ...\n- Item 2: ...\n- Recommendation: ...", + "model_used": "gpt-4-turbo", + "tokens_used": 800 + }, + "created_at": "2026-04-11T16:51:20.404640", + "completed_at": "2026-04-11T16:51:20.404960" + }, + "executor_id": "565c7f6d-e2af-4eef-a037-49a66ea9fda7", + "result": { + "analysis_result": "Analysis of: Analyze stakeholders and create stakeholder register\n\nKey findings: \n- Item 1: ...\n- Item 2: ...\n- Recommendation: ...", + "model_used": "gpt-4-turbo", + "tokens_used": 800 + } + } + ], + "experience_manager_log": [ + { + "timestamp": "2026-04-11T16:51:20.404716", + "agent_id": "exp-001", + "task_id": "df4a59bf-0140-4d91-84c6-427ab23ad750", + "task_type": "data_operation", + "input": { + "task_type": "document_generation", + "keywords": "Generate project charter based on user input" + }, + "output": { + "context": { + "recent_executions": [], + "knowledge_base_snapshot": { + "analysis_134": { + "task_description": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线", + "successful_input_pattern": {}, + "successful_output_pattern": { + "atomic_tasks": [ + { + "id": "4ff13ffa-980e-48ff-bce6-163b589e69b1", + "task_type": "document_generation", + "description": "Generate project charter based on user input", + "input_data": { + "template": "project_charter", + "user_input": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线" + }, + "expected_output_schema": { + "sections": [ + "目标", + "范围", + "里程碑", + "干系人" + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404439", + "completed_at": null + }, + { + "id": "d582bdbe-47e4-41db-9ae9-98e4fc1b893b", + "task_type": "analysis", + "description": "Analyze stakeholders and create stakeholder register", + "input_data": { + "charter_placeholder": "will be filled" + }, + "expected_output_schema": { + "stakeholders": [ + { + "name": "", + "role": "", + "power": 0, + "interest": 0 + } + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404475", + "completed_at": null + } + ] + }, + "count": 1 + } + } + }, + "message": "Context provided for task execution" + }, + "status": "completed" + }, + { + "timestamp": "2026-04-11T16:51:20.404800", + "agent_id": "116c77a3-f8ac-4c14-9e12-e1e7098898c3", + "task_id": "4ff13ffa-980e-48ff-bce6-163b589e69b1", + "task_type": "document_generation", + "input": { + "template": "project_charter", + "user_input": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线" + }, + "output": { + "generated_document": "# Sample Document\n\nThis is a generated document for: Generate project charter based on user input\n\nSections: ['sections']", + "model_used": "claude-3-sonnet", + "tokens_used": 1500 + }, + "status": "completed" + }, + { + "timestamp": "2026-04-11T16:51:20.404902", + "agent_id": "exp-001", + "task_id": "d32dfb0d-62e3-4564-9b47-f1a3c127e657", + "task_type": "data_operation", + "input": { + "task_type": "analysis", + "keywords": "Analyze stakeholders and create stakeholder register" + }, + "output": { + "context": { + "recent_executions": [ + { + "timestamp": "2026-04-11T16:51:20.404716", + "agent_id": "exp-001", + "task_id": "df4a59bf-0140-4d91-84c6-427ab23ad750", + "task_type": "data_operation", + "input": { + "task_type": "document_generation", + "keywords": "Generate project charter based on user input" + }, + "output": { + "context": { + "recent_executions": [], + "knowledge_base_snapshot": { + "analysis_134": { + "task_description": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线", + "successful_input_pattern": {}, + "successful_output_pattern": { + "atomic_tasks": [ + { + "id": "4ff13ffa-980e-48ff-bce6-163b589e69b1", + "task_type": "document_generation", + "description": "Generate project charter based on user input", + "input_data": { + "template": "project_charter", + "user_input": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线" + }, + "expected_output_schema": { + "sections": [ + "目标", + "范围", + "里程碑", + "干系人" + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404439", + "completed_at": null + }, + { + "id": "d582bdbe-47e4-41db-9ae9-98e4fc1b893b", + "task_type": "analysis", + "description": "Analyze stakeholders and create stakeholder register", + "input_data": { + "charter_placeholder": "will be filled" + }, + "expected_output_schema": { + "stakeholders": [ + { + "name": "", + "role": "", + "power": 0, + "interest": 0 + } + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404475", + "completed_at": null + } + ] + }, + "count": 1 + } + } + }, + "message": "Context provided for task execution" + }, + "status": "completed" + } + ], + "knowledge_base_snapshot": { + "analysis_134": { + "task_description": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线", + "successful_input_pattern": {}, + "successful_output_pattern": { + "atomic_tasks": [ + { + "id": "4ff13ffa-980e-48ff-bce6-163b589e69b1", + "task_type": "document_generation", + "description": "Generate project charter based on user input", + "input_data": { + "template": "project_charter", + "user_input": "创建项目章程，目标是开发一个新的客户管理系统，范围包括需求、开发、测试和上线" + }, + "expected_output_schema": { + "sections": [ + "目标", + "范围", + "里程碑", + "干系人" + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404439", + "completed_at": null + }, + { + "id": "d582bdbe-47e4-41db-9ae9-98e4fc1b893b", + "task_type": "analysis", + "description": "Analyze stakeholders and create stakeholder register", + "input_data": { + "charter_placeholder": "will be filled" + }, + "expected_output_schema": { + "stakeholders": [ + { + "name": "", + "role": "", + "power": 0, + "interest": 0 + } + ] + }, + "status": "pending", + "agent_id": null, + "result": null, + "created_at": "2026-04-11T16:51:20.404475", + "completed_at": null + } + ] + }, + "count": 1 + }, + "data_operation_119": { + "task_description": "Get context for task execution", + "successful_input_pattern": { + "task_type": "document_generation", + "keywords": "Generate project charter based on user input" + }, + "successful_output_pattern": { + "context": { + "recent_executions": [], + "knowledge_base_snapshot": { + "analysis_134": { + 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