Harden PMFlow context binding and world model snapshots

CLAUDE.md

@@ -63,7 +63,8 @@ backend/
 │       ├── 20260324_add_project_create_idempotency_indexes.py # 项目创建幂等索引：活动项目标题大小写无关唯一
 │       ├── 20260327_cleanup_model_routing_legacy_names.py # model_routing 旧函数名清理与重命名迁移
 │       ├── 20260405_add_mounted_ai_runtime_tables.py # Mounted Runtime 建表迁移
-│       └── 20260405_add_mounted_runtime_plan_state_fields.py # Mounted Runtime plan_status / conversation_scope_key 字段
+│       ├── 20260405_add_mounted_runtime_plan_state_fields.py # Mounted Runtime plan_status / conversation_scope_key 字段
+│       └── 20260406_add_mounted_runtime_active_draft_fields.py # Mounted draft active work item / message anchor 字段
 ├── scripts/                   # 后端运维/数据修复脚本
 │   ├── backfill_orphan_signal_links.py               # 回填孤立 signal 的 project_signal_links
 │   ├── dev/                   # 开发辅助脚本（check_users/seed_all）
@@ -196,19 +197,21 @@ backend/
 - 新增：`backend/app/utils/project_command_utils.py`（项目创建标题/描述提取与归一化辅助）
 - 新增：`backend/app/services/ai_pmo_intent_routing.py`（AI PMO 意图/路由常量与解析器收口，统一 `family.action` 路由映射）
 - 新增：`backend/app/services/ai_pmo/workflow_semantic_planner/`（统一业务语义规划层：唯一允许承载 workflow bundle 语义关键词/正则/兼容 helper 的目录）
+- 新增：`backend/app/services/ai_pmo/semantic_context/`（OpenCow 风格语义上下文子模块：统一 `SemanticContextPacket/ModeCard/SemanticDecision/DecisionGateResult` 契约、`SemanticContextAssembler`、`SemanticDecisionGatekeeper` 与 `SemanticDecisionService`；当前已接入 ingress shadow 观测，并为 intent/query-scene 主链提供 direct metadata 替代语义入口）
 - 新增：`backend/app/models/reply_iteration.py`（回复质量迭代模型：`QualityAsset/QualityAssetPair/JudgeRun/OptimizerCandidate/PromptRelease`）
 - 新增：`backend/app/models/training_pipeline.py`（训练/微调管线模型：`TrainingDataset/TrainingJob/TrainingEvaluation/TrainedModelArtifact/TrainedModelDeployment`）
-- 新增：`backend/app/models/mounted_ai_runtime.py`（Mounted Runtime 运行时模型：`MountedTopicWorkspace/MountedDraftWorkspace/MountedConversationRun/MountedAgentTrace/MountedSourceActivation/MountedToolCapabilityPolicy`；`MountedTopicWorkspace` 额外承载 `conversation_scope_key + plan_status`，用于同群同人 topic 隔离与 plan 状态机）
+- 新增：`backend/app/models/mounted_ai_runtime.py`（Mounted Runtime 运行时模型：`MountedTopicWorkspace/MountedDraftWorkspace/MountedConversationRun/MountedAgentTrace/MountedSourceActivation/MountedToolCapabilityPolicy`；`MountedTopicWorkspace` 额外承载 `conversation_scope_key + plan_status`，用于同群同人 topic 隔离与 plan 状态机；`MountedDraftWorkspace` 额外承载 `active_work_item_id + latest_source_message_id + latest_reply_message_id`，用于当前 active draft 绑定与 reply anchor 锚定）
 - 新增：`backend/app/services/reply_iteration/service.py`（反馈中心提升为学习资产、裁判分桶、候选改动、prompt release trial 服务；支持 `backfill_mounted_training_assets_from_feedback`：从真实 `feedback_center_records + qa_records` 回填 golden/failure、preference pair 与 judge runs，供 mounted runtime 真实业务训练使用）
 - 新增：`backend/app/services/training_pipeline/service.py`（训练数据集、训练作业、评测、artifact promote、deployment 决策服务；支持 `bootstrap_mounted_training_wave`：自动从 mounted runtime 质量资产生成 `teacher_dev_set / teacher_eval_frozen_set / sft / preference`，并启动 low/high 训练波次；mounted wave 会立刻消费本次 queued jobs，产出 completed job/evaluation/acceptance gate；当 failure 样本带 `corrected_answer` 时会自动合成 synthetic golden / preference pair）
 - 新增：`backend/app/services/prompt_release/service.py`（只读 QA prompt release 版本管理与 serving 渲染优先级）
-- 新增：`backend/app/services/mounted_ai_runtime_service.py`、`backend/app/services/mounted_ai_runtime_write_service.py`（Mounted Runtime 读模型与写路径：来源/主题/run 查询、source activation / tool policies / training ops / hard cases / replay-run 管理，以及 ingress topic/run/trace/source-activation 落库；当某来源存在 active activation 白名单时，仅命中的 scope 才进入 mounted tracking；run detail 会返回 rich trace：`input_summary/output_summary/tool_trace`；写路径会持久化 `conversation_scope_key` 与显式 `plan_status`）
+- 新增：`backend/app/services/mounted_ai_runtime_service.py`、`backend/app/services/mounted_ai_runtime_write_service.py`（Mounted Runtime 读模型与写路径：来源/主题/run 查询、source activation / tool policies / training ops / hard cases / replay-run 管理，以及 ingress topic/run/trace/source-activation 落库；当某来源存在 active activation 白名单时，仅命中的 scope 才进入 mounted tracking；run detail 会返回 rich trace：`input_summary/output_summary/tool_trace`，并暴露 `topic/draft/semantic_packet_summary/semantic_decision/semantic_gate_result/semantic_diff`；写路径会持久化 `conversation_scope_key`、显式 `plan_status` 与 semantic shadow 观测结果）
 - 新增：`backend/app/api/ai_steward_api/routes_reply_iteration.py`（AI Steward 回复质量闭环接口：反馈提升、judge-runs、candidates、prompt-releases、trials，以及 `POST /ai-steward/reply-iteration/backfill-mounted-assets` 真实业务训练资产回填入口）
 - 新增：`backend/app/api/ai_steward_api/routes_training_pipeline.py`（AI Steward 训练管线接口：datasets、jobs、evaluations、artifacts、deployments，以及 `POST /ai-steward/training/mounted-wave` 一键训练波次入口、`POST /ai-steward/training/jobs/{id}/run` 单 job 执行入口）
 - 新增：`backend/app/api/ai_steward_api/routes_mounted_ai.py`、`backend/app/schemas/mounted_ai_runtime.py`（Mounted Runtime 控制台接口：sources/source-activations/tool-policies/topics/runs/run-detail/cutover-status/eval-suites/training-ops/hard-cases，以及 admin/source-activation、admin/tool-policy、admin/replay-run）
 - 新增：`backend/alembic/versions/20260327_add_reply_iteration_training_tables.py`（回复质量迭代/训练管线主迁移：学习资产表、prompt release、训练数据集、训练作业与 experiment 扩展字段）
 - 新增：`backend/alembic/versions/20260405_add_mounted_ai_runtime_tables.py`（Mounted Runtime 建表迁移：topic workspace、draft workspace、conversation run、agent trace、source activation、tool capability policy）
 - 新增：`backend/alembic/versions/20260405_add_mounted_runtime_plan_state_fields.py`（Mounted Runtime 计划态增强迁移：`mounted_topic_workspaces` 新增 `conversation_scope_key / plan_status`，支撑同群同人多 topic 隔离与 plan 状态机）
+- 新增：`backend/alembic/versions/20260406_add_mounted_runtime_active_draft_fields.py`（Mounted Runtime active draft 锚点迁移：`mounted_draft_workspaces` 新增 `active_work_item_id / latest_source_message_id / latest_reply_message_id`，支撑 session-first 草稿确认与最新回包锚定）
 - 新增：`backend/tests/test_reply_iteration_service.py`、`test_training_pipeline_service.py`、`test_reply_iteration_api.py`、`test_reply_iteration_training_migration.py`（回复质量闭环与训练管线基础回归）
 - 新增：`backend/tests/test_mounted_ai_runtime_models.py`、`test_mounted_ai_runtime_migration.py`、`test_mounted_ai_runtime_api.py`、`test_mounted_ai_runtime_write_service.py`（Mounted Runtime 模型/迁移/API/写路径回归）
 - 新增：`backend/app/services/ai_pmo/ai_pmo_kernel/ai_pmo_workflow_runtime.py`（AI PMO `ContextAssembler` + `WorkflowRegistry/WorkflowHandler` 运行时骨架）
@@ -228,10 +231,11 @@ backend/
 - 更新：`backend/app/services/signal/signal_capture_service/mixin_capture_entry.py`（Feishu capture 在 source activation 存在 active 白名单规则时，未命中 scope 的 chat 直接返回 `mounted_runtime_scope_not_activated`，不再继续进入 kernel/queue）
 - 更新：`backend/app/services/signal/signal_capture_service/mixin_capture_entry.py`（kernel 未处理或 capture 主链异常时会回滚当前 session 并把真实失败原因写入 `direct_reply_text`，不再回退统一“系统处理中出现异常”伪成功文案）
 - 更新：`backend/app/services/ai_pmo/ai_pmo_kernel_ingress_flow/mixin_auto.py`（写操作改为“先起草后确认”，状态进入 `draft_pending_confirm`）
-- 更新：`backend/app/services/ai_pmo/ai_pmo_kernel_ingress_flow/mixin_entry.py`（会话命中 `draft_pending_confirm` 时优先路由草稿确认处理，并把 ingress 主链同步写入 Mounted Runtime 的 topic/run/trace；除主脑 `ConversationOrchestrator` 外，还会额外记录 `PlanCoach/Clarifier/DraftSynthesizer/Executor` 等 specialist trace；`query.answer` + `business_review/assignee` 场景会在 runtime 中收口到 `plan/chat`，不再伪装成 `execute/Executor`；kernel 失败改为 `logger.exception` 保留完整 traceback）
+- 更新：`backend/app/services/ai_pmo/ai_pmo_kernel_ingress_flow/mixin_entry.py`（会话命中 `draft_pending_confirm` 时优先路由草稿确认处理，并把 ingress 主链同步写入 Mounted Runtime 的 topic/run/trace；除主脑 `ConversationOrchestrator` 外，还会额外记录 `PlanCoach/Clarifier/DraftSynthesizer/Executor` 等 specialist trace；`query.answer` + `business_review/assignee` 场景会在 runtime 中收口到 `plan/chat`，不再伪装成 `execute/Executor`；query-scene 强制改为优先读取 direct metadata，不再在主链回填 `semantic_bundle`；kernel 失败改为 `logger.exception` 保留完整 traceback）
-- 更新：`backend/app/services/ai_pmo/ai_pmo_typed_executor_execute/mixin_execute_query_project.py`（query `failed_safe` 只在 SQLAlchemy session 已进入 failed/inactive 状态时回滚，避免把同链路的 ingest/work_item 一起回滚掉；需求收敛/范围建议/负责人推荐/项目存在性查询会优先走 deterministic 直答，不再默认落到 heavy RAG；“一个 AI 客服工具，这个项目有吗”这类问句会直接返回项目事实库命中结果）
+- 更新：`backend/app/services/ai_pmo/ai_pmo_typed_executor_execute/mixin_execute_query_project.py`（query `failed_safe` 只在 SQLAlchemy session 已进入 failed/inactive 状态时回滚，避免把同链路的 ingest/work_item 一起回滚掉；需求收敛/范围建议/负责人推荐/项目存在性查询会优先走 deterministic 直答，不再默认落到 heavy RAG；query executor 对 `message_intent/consultant_mode_preference/advise_subtype/force_thinking_mode` 改为 direct metadata first，旧 `semantic_bundle.diagnostics` 仅保留兼容兜底；`requirement_plan` followup 在存在 active workspace 时优先按 mounted runtime 状态与字段增量续接，不再先依赖旧 planner 猜 continuation；“一个 AI 客服工具，这个项目有吗”这类问句会直接返回项目事实库命中结果）
+- 更新：`backend/app/services/ai_pmo/ai_pmo_kernel/mixins/intent/ai_pmo_kernel_mixins_intent_resolve.py`、`ai_pmo_kernel_mixins_intent_llm.py`、`backend/app/services/ai_pmo/ai_pmo_kernel/mixins/ingress/ai_pmo_kernel_mixins_ingress_render.py`（意图解析主链移除 `signal_source.onboard` 的 pre-LLM 规则锁；LLM metadata 不再注入 `semantic_bundle`；capture result 仅生成 `direct_metadata_compat` 形式的兼容语义包，不再为回包临时重跑 `workflow_semantic_planner`）
 - 更新：`backend/app/services/adaptive_rag_service/mixin_followup_anchor.py`、`mixin_fast_answer_project_main.py`（standalone 项目存在性问句不再误判成 referential followup；项目风险/阻塞问句在无任务拆解时返回“缺任务分解、缺里程碑、缺负责人推进节奏”的风险代理信号，不再给空泛兜底）
-- 更新：`backend/app/services/ai_pmo/ai_pmo_typed_executor_discovery/mixin_discover.py`、`backend/app/services/ai_pmo/ai_pmo_kernel/mixins/intent/ai_pmo_kernel_mixins_intent_resolve.py`（`signal_source.onboard` 显式接入请求会在 LLM 前锁定 intent，禁止再漂移成 `project_signal.discover`；discovery mixin 补齐 `_safe_int/_utc_now_naive` 导入，消除真实 discovery 执行时的 `NameError`）
+- 更新：`backend/app/services/ai_pmo/ai_pmo_typed_executor_discovery/mixin_discover.py`、`backend/app/services/ai_pmo/ai_pmo_kernel/mixins/intent/ai_pmo_kernel_mixins_intent_resolve.py`（discovery mixin 补齐 `_safe_int/_utc_now_naive` 导入，消除真实 discovery 执行时的 `NameError`；`signal_source.onboard` 不再走 pre-LLM 规则锁，主链保持 model-only，再由后续 route/gate 约束）
 - 更新：`backend/app/services/ai_pmo/ai_pmo_kernel/mixins/intent/ai_pmo_kernel_mixins_intent_rules.py`（canonical intent builder 现在保留 `project.update/task.* / personnel.* / knowledge.* / notification.*` 等 mutation intent，避免 LLM 已识别的写操作被错误降级成 `ignore`）
 - 更新：`backend/app/models/work_item.py`（新增 `draft_content/draft_expires_at/draft_version/confirm_message_id`）
 - 新增：`backend/app/services/ai_pmo/ai_pmo_kernel/compositions/world_model.py`（Kernel world-model 组合入口）
@@ -399,8 +403,8 @@ frontend/
 - 更新：`frontend/src/pages/CharterDetailPage.tsx`、`ContractSyncBoardPage.tsx`、`DashboardPage.tsx`、`KnowledgeGraphContextBoardPage.tsx`、`LoginPage.tsx`、`ModelRoutingConfigPage.tsx`、`SchedulerCenterPage.tsx`、`SkillTasksPage.tsx`、`SkillsPage.tsx`、`TaskBriefCenterPage.tsx`、`TaskBriefRunsPage.tsx`、`TaskBriefTemplatePage.tsx`、`UserProfilePage.tsx`、`UserSkillsPage.tsx`、`WorkloadDetailPage.tsx`、`WorkloadsPage.tsx`（统一降级为轻量入口，转发到 `pages/*/` 子目录）
 - 新增：`frontend/src/pages/charter-detail/CharterDetailPage.tsx`、`contract-sync-board/ContractSyncBoardPage.tsx`、`dashboard/DashboardPage.tsx`、`knowledge-graph-context-board/KnowledgeGraphContextBoardPage.tsx`、`login/LoginPage.tsx`、`model-routing-config/ModelRoutingConfigPage.tsx`（剩余核心页面目录化收敛）
 - 新增：`frontend/src/pages/scheduler-center/SchedulerCenterPage.tsx`、`skill-tasks/SkillTasksPage.tsx`、`skills/SkillsPage.tsx`、`task-brief-center/TaskBriefCenterPage.tsx`、`task-brief-runs/TaskBriefRunsPage.tsx`、`task-brief-template/TaskBriefTemplatePage.tsx`（任务/调度/技能页面目录化收敛）
-- 新增：`frontend/src/pages/scheduler-center/components/MountedAISourceStatusPanel.tsx`、`MountedAISourceActivationPanel.tsx`、`MountedAIToolGuardrailsPanel.tsx`、`MountedAITopicsPanel.tsx`、`MountedAIRunsPanel.tsx`、`MountedAIRunReplayPanel.tsx`、`MountedAIOrchestrationGraphPanel.tsx`、`MountedAIContextLayersPanel.tsx`、`MountedAITrainingOpsPanel.tsx`、`MountedAIEvalScoreboardPanel.tsx`、`MountedAIHardCasesPanel.tsx`、`MountedAIAcceptancePanel.tsx`（Mounted AI Console 来源状态、白名单 cutover、tool guardrails、topic workspace、run 列表、replay 详情、agent 编排图、context layers、training ops、eval score board、hard cases 与验收门槛面板；其中 source activation / tool guardrails 已支持前台编辑，run replay 支持直接触发 `admin/replay-run`，training ops 已支持直接发起 mounted training wave，并展示 recent datasets / recent jobs / acceptance gate，replay/orchestration 已展示 rich trace 的 `input/output/tool` 明细）
+- 新增：`frontend/src/pages/scheduler-center/components/MountedAISourceStatusPanel.tsx`、`MountedAISourceActivationPanel.tsx`、`MountedAIToolGuardrailsPanel.tsx`、`MountedAITopicsPanel.tsx`、`MountedAIRunsPanel.tsx`、`MountedAIRunReplayPanel.tsx`、`MountedAIOrchestrationGraphPanel.tsx`、`MountedAIContextLayersPanel.tsx`、`MountedAISemanticPacketPanel.tsx`、`MountedAISemanticDecisionPanel.tsx`、`MountedAIShadowDiffPanel.tsx`、`MountedAITrainingOpsPanel.tsx`、`MountedAIEvalScoreboardPanel.tsx`、`MountedAIHardCasesPanel.tsx`、`MountedAIAcceptancePanel.tsx`（Mounted AI Console 来源状态、白名单 cutover、tool guardrails、topic workspace、run 列表、replay 详情、agent 编排图、context layers、semantic packet / decision / shadow diff、training ops、eval score board、hard cases 与验收门槛面板；其中 source activation / tool guardrails 已支持前台编辑，run replay 支持直接触发 `admin/replay-run`，training ops 已支持直接发起 mounted training wave，并展示 recent datasets / recent jobs / acceptance gate）
-- 更新：`frontend/src/pages/scheduler-center/SchedulerCenterPage.tsx`（Scheduler Center 升级为 Mounted AI Console：接入 mounted runtime 查询与 admin actions，支持 source activation、tool policy、run replay、training wave 的前台操作，并支持通过 `mountedRunId` 查询参数直接定位 run）
+- 更新：`frontend/src/pages/scheduler-center/SchedulerCenterPage.tsx`（Scheduler Center 升级为 Mounted AI Console：接入 mounted runtime 查询与 admin actions，支持 source activation、tool policy、run replay、training wave 的前台操作，并支持通过 `mountedRunId` 查询参数直接定位 run；run detail 区新增 semantic packet / decision / gate / shadow diff 观测区）
 - 新增：`frontend/src/pages/user-profile/UserProfilePage.tsx`、`user-skills/UserSkillsPage.tsx`、`workload-detail/WorkloadDetailPage.tsx`、`workloads/WorkloadsPage.tsx`（用户与负载页面目录化收敛）
 - 更新：`frontend/src/pages/InboxPage.tsx`（降级为轻量入口，转发到 `pages/inbox/InboxPage`）
 - 新增：`frontend/src/pages/inbox/helpers.ts`（Inbox 页面 tab 过滤、排序、project-scope 判断 helper）
@@ -578,12 +582,16 @@ uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
 - `workflow_semantic_planner` 也必须把通用需求收敛里的短槽位回答继续识别为 `message_intent=requirement_plan`，例如“先给内部 PM 和研发负责人用 / 飞书总结就行 / 每次会后半小时内整理出来”；这类碎片化回复不得因为没有新 `semantic_bundle` 就退回普通 query
 - `ExecuteQueryProjectMixin` 对 `message_intent=requirement_plan` 的查询必须优先走 mounted runtime 直答快路径：默认只输出自然短话术，每轮最多只保留 1 个关键问题；当 `scoring_basis/target_user/output_form/cadence/first_scope` 收集完成时，必须直接整理成需求草稿，禁止继续空转追问
 - `requirement_plan` 的 Mounted Runtime 状态必须显式落库到 `MountedTopicWorkspace.plan_status`：至少区分 `idle / collecting / options_ready / plan_ready / awaiting_execute_confirm / suspended`，禁止只靠 `draft.state` 或 `metadata_json` 猜当前阶段
+- 新的 OpenCow 风格语义上下文链必须先组装 `SemanticContextPacket`，再由模型返回 `SemanticDecision`，最后由 gatekeeper 决定 `plan_status`；当前 shadow 结果已进入 Mounted Runtime 观测，同时 intent/query-scene 主链已切到 direct metadata，不得再把 `semantic_bundle` 当作主链真源或绕过统一执行门禁
 - 正在收敛中的 `requirement_plan` 必须支持普通人碎片化回复：像“技术指标吧 / 给投顾看 / 飞书日报就行 / 每天收盘后发”这种单字段短句，即使缺少新的 `semantic_bundle`，只要同线程/同群同人存在 active plan workspace，也必须自动续接当前需求收敛，不得回落到通用 query 或高阶 RAG
 - `requirement_plan` 的追问节奏必须像自然聊天：上一轮只确认新增信息、下一轮只问一个缺口；初始描述中的“每日/自动”不能过早推断成已确认的更新频率，避免少问关键问题或提前出草稿
 - `requirement_plan` 必须按 profile 区分场景：`scoring` 需求继续收集 `打分依据/目标用户/输出形式/更新频率/第一版范围`，`generic` 需求收集 `输入材料/目标用户/输出形式/响应时效/第一版范围`；像“会议纪要提待办”这类普通工具需求，禁止再复用“这个分数怎么打”的旧问题模板
+- `generic requirement_plan` 的 `A/B/C` 选项只能出现在 active requirement session 的 `collecting` 轮次里，并且只允许绑定当前缺口字段（如 `target_user / output_form / cadence`）；普通闲聊、query.answer、非 requirement followup 必须继续保持自由文案，禁止全局套用选项模板
+- 用户显式说“聊个新需求 / 另一个需求 / 跟上一个无关”时，AI PMO 必须先清掉当前会话里继承的旧 `current_project_id`，避免后续短回复继续被旧项目上下文污染
+- 裸 `A/B/C/确认/你自行推荐` 这类低信息量回复，如果没有命中当前 active requirement session 或待确认草稿，必须直接提示“当前没有进行中的收敛轮次/草稿”，禁止再回落到 `query.answer` 检索图谱并生成旧项目结论
 - 飞书私聊中的“答错了/不对/调用高级模型/用更强模型/重新回答”必须被识别为 `correction_escalation` 指令：优先复用上一轮上下文主题并强制深度回答，禁止把这类话术当成新的系统配置查询
 - `POST /rag/ask` 在 `rag_request_context` 或统一语义规划层命中 `business_review/correction_escalation` 时，必须强制留在 `query.read_only` 主链并进入对应 QA Prompt；禁止因为 LLM 意图分类把“分析/MVP/重答”误送到 `signal.create -> draft_pending_confirm`
-- Feishu 新消息即使被低阶 LLM 先判成 `project.create`，只要统一语义规划层从原句识别出 `requirement_plan/business_review/correction_escalation`，就必须在 ingress `_coerce_intent_for_query_scene` 阶段强制拉回 `query.answer`；Mounted Runtime 记录 `mode_after/agent` 时也必须优先读取 `semantic_bundle.diagnostics`，禁止因顶层 `route_metadata` 缺字段把真实 `plan` 记成 `chat`
+- Feishu 新消息即使被低阶 LLM 先判成 `project.create`，只要统一语义规划层从原句识别出 `requirement_plan/business_review/correction_escalation`，就必须在 ingress `_coerce_intent_for_query_scene` 阶段强制拉回 `query.answer`；Mounted Runtime 记录 `mode_after/agent` 时必须优先读取顶层 direct metadata，`semantic_bundle.diagnostics` 只允许作为兼容回退，禁止因顶层 `route_metadata` 缺字段把真实 `plan` 记成 `chat`
 - 飞书入口的高精度同步判定不能只看 `primary_workflow`；只要 `WorkflowSemanticPlanner` 的 `diagnostics.message_intent=requirement_plan`，就必须优先返回 `semantic:requirement_plan` 并直接同步进入 AI PMO kernel，转发 `semantic_bundle`，禁止再先入异步队列发送 `已收到，正在处理` ACK
 - “高级模型是否启用/模型路由是否启用/当前是否高配”这类运行态问题，只有命中运行态配置、路由快照、引擎配置或真实调用记录时才允许下结论；若证据只有页面目录、代码图谱或功能清单，回复必须明确为“无法仅凭静态资料确认”
 - 通用行业趋势问答（如“宠物行业最新动态”）不得注入数据库、项目历史、用户历史或外部实时搜索结果；只有显式带项目上下文时才允许走 `external_trend`，对“不要提项目/别提项目”这类否定语句不能误判为项目上下文
@@ -612,7 +620,7 @@ uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
 - `model-routing` 只暴露逻辑层字段：`function_name/tier_alias/source/rule_name/rule_priority/last_execution`；`PUT` 仅接受 `tier_alias=low|high`
 - 回复质量闭环接口：参考 `backend/app/api/ai_steward_api/routes_reply_iteration.py`（`POST /ai-steward/feedback-center/items/{id}/promote`、`POST /ai-steward/reply-iteration/backfill-mounted-assets`、`/reply-iteration/judge-runs/candidates/prompt-releases/trials`）
 - 训练/微调管线接口：参考 `backend/app/api/ai_steward_api/routes_training_pipeline.py`（`GET/POST /ai-steward/training/datasets/jobs`、`POST /training/jobs/{id}/run`、`GET /training/jobs/{id}/evaluations`、`POST /training/artifacts/{id}/promote`、`POST /training/deployments/{id}/decision`、`POST /training/mounted-wave`；mounted wave 会自动生成 `teacher_dev_set / teacher_eval_frozen_set / sft / preference` 并启动并执行 low/high 训练波次）
-- Mounted Runtime 控制台接口：参考 `backend/app/api/ai_steward_api/routes_mounted_ai.py`（`GET /ai-steward/mounted-ai/sources|source-activations|tool-policies|topics|runs|runs/{id}|cutover-status|eval-suites|eval-suites/{id}|training-ops|hard-cases`，以及 `POST /ai-steward/mounted-ai/admin/source-activation|tool-policy|replay-run`，统一 `{code,message,data}` 包络）；其中 `cutover-status` 必须返回模块级旧脑退役视图：`legacy_cutover_state / retired_item_count / active_item_count / legacy_cutover_items[]`
+- Mounted Runtime 控制台接口：参考 `backend/app/api/ai_steward_api/routes_mounted_ai.py`（`GET /ai-steward/mounted-ai/sources|source-activations|tool-policies|topics|runs|runs/{id}|cutover-status|eval-suites|eval-suites/{id}|training-ops|hard-cases`，以及 `POST /ai-steward/mounted-ai/admin/source-activation|tool-policy|replay-run`，统一 `{code,message,data}` 包络）；其中 `runs/{id}` 必须返回 `topic/draft/semantic_packet_summary/semantic_decision/semantic_gate_result/semantic_diff`，`cutover-status` 必须返回模块级旧脑退役视图：`legacy_cutover_state / retired_item_count / active_item_count / legacy_cutover_items[]`
 - 任务包中心接口：参考 `backend/app/api/task_briefs.py`（`/task-briefs/center/templates/runs/generate/contract-sync`）
 - KG 上下文闭环接口：参考 `backend/app/api/knowledge_graph.py`（`GET /ai-steward/knowledge-graph/capability-summary`、`POST /ai-steward/knowledge-graph/verify-context-consumption`、`POST /ai-steward/knowledge-graph/sync/work-items`；旧 `POST /ai-steward/knowledge-graph/sync/feishu` 仅保留兼容别名）
 - 需求点落信号源：参考 `backend/app/services/signal/signal_source_orchestration/mixin_registry_execute.py`（`materialize_requirement_sources` 负责归一、去重、`raw_context` 持久化，并为每条 registry 同步绑定/物化正式 `Signal`）
@@ -690,6 +698,8 @@ uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
 - World Model 静态层已接入 Redis 缓存（key: `wm:static:{tenant_id}:{project_id}`，TTL 600s）
 - 写操作后主动 invalidate，动态层不缓存
 - AI PMO 意图识别已切换 `model-only`：`_resolve_intent` 仅接受 LLM 结构化结果，不再走规则/正则兜底链路
+- `semantic_context` 新链当前状态：ingress shadow 观测已落库，intent/query-scene/render 主链已切到 direct metadata，旧 `semantic_bundle/workflow_semantic_planner` 仍仅在部分 requirement-plan executor / followup 兼容路径中生效；后续删旧规则时应继续以 Mounted Runtime 观测页核对 shadow diff
+- IT 五轮 requirement plan 现以 `MountedDraftWorkspace.active_work_item_id + latest_*_message_id` 锚定当前 active draft；历史未绑定这些字段的旧草稿不会自动认领，需先重新生成当前草稿后再确认/修改
 - AI PMO / Feishu 执行门已取消置信度阈值拦截与高风险人工复核：`confidence` 仅保留在 `decision_trace` / processing log 中作观测，不再作为自动执行的 reject 条件；当前门禁仅保留 `force_manual` 与 `correction_action`
 - 模型默认值与 provider/tier 映射以 `backend/.openclaw/pmflow-routing-rules.json` 为唯一配置源；`.env` / `.env.example` 仅保留 API key 与运行开关，`OPENCLAW_ROUTING_RULES_PATH` 只用于定位这份文件
 - 调用层与路由页面只使用 `low/high` 两层语义；`safety` 已废弃并从配置校验、运行时解析、前端选项中移除

backend/alembic/versions/20260406_add_mounted_runtime_active_draft_fields.py

@@ -0,0 +1,116 @@
+"""add mounted runtime active draft fields
+
+Revision ID: 20260406_add_mounted_runtime_active_draft_fields
+Revises: 20260405_add_mounted_runtime_plan_state_fields
+Create Date: 2026-04-06
+"""
+
+from __future__ import annotations
+
+from alembic import op
+import sqlalchemy as sa
+
+
+revision = "20260406_add_mounted_runtime_active_draft_fields"
+down_revision = "20260405_add_mounted_runtime_plan_state_fields"
+branch_labels = None
+depends_on = None
+
+
+def _column_exists(bind, table_name: str, column_name: str) -> bool:
+    inspector = sa.inspect(bind)
+    try:
+        columns = inspector.get_columns(table_name)
+    except Exception:
+        return False
+    return any(str(column.get("name")) == column_name for column in columns)
+
+
+def _index_exists(bind, table_name: str, index_name: str) -> bool:
+    inspector = sa.inspect(bind)
+    try:
+        indexes = inspector.get_indexes(table_name)
+    except Exception:
+        return False
+    return any(str(index.get("name")) == index_name for index in indexes)
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+
+    if not _column_exists(bind, "mounted_draft_workspaces", "active_work_item_id"):
+        op.add_column(
+            "mounted_draft_workspaces",
+            sa.Column("active_work_item_id", sa.BigInteger(), nullable=True),
+        )
+    if not _column_exists(bind, "mounted_draft_workspaces", "latest_source_message_id"):
+        op.add_column(
+            "mounted_draft_workspaces",
+            sa.Column("latest_source_message_id", sa.VARCHAR(length=128), nullable=True),
+        )
+    if not _column_exists(bind, "mounted_draft_workspaces", "latest_reply_message_id"):
+        op.add_column(
+            "mounted_draft_workspaces",
+            sa.Column("latest_reply_message_id", sa.VARCHAR(length=128), nullable=True),
+        )
+
+    if not _index_exists(bind, "mounted_draft_workspaces", "ix_mounted_draft_workspaces_active_work_item_id"):
+        op.create_index(
+            "ix_mounted_draft_workspaces_active_work_item_id",
+            "mounted_draft_workspaces",
+            ["active_work_item_id"],
+        )
+    if not _index_exists(bind, "mounted_draft_workspaces", "ix_mounted_draft_workspaces_latest_source_message_id"):
+        op.create_index(
+            "ix_mounted_draft_workspaces_latest_source_message_id",
+            "mounted_draft_workspaces",
+            ["latest_source_message_id"],
+        )
+    if not _index_exists(bind, "mounted_draft_workspaces", "ix_mounted_draft_workspaces_latest_reply_message_id"):
+        op.create_index(
+            "ix_mounted_draft_workspaces_latest_reply_message_id",
+            "mounted_draft_workspaces",
+            ["latest_reply_message_id"],
+        )
+
+    op.execute(
+        sa.text(
+            """
+            UPDATE mounted_draft_workspaces AS draft
+            SET latest_source_message_id = COALESCE(
+                NULLIF(draft.latest_source_message_id, ''),
+                (
+                    SELECT topic.last_message_id
+                    FROM mounted_topic_workspaces AS topic
+                    WHERE topic.id = draft.topic_workspace_id
+                )
+            )
+            WHERE COALESCE(NULLIF(draft.latest_source_message_id, ''), '') = ''
+            """
+        )
+    )
+
+
+def downgrade() -> None:
+    bind = op.get_bind()
+    if _index_exists(bind, "mounted_draft_workspaces", "ix_mounted_draft_workspaces_latest_reply_message_id"):
+        op.drop_index(
+            "ix_mounted_draft_workspaces_latest_reply_message_id",
+            table_name="mounted_draft_workspaces",
+        )
+    if _index_exists(bind, "mounted_draft_workspaces", "ix_mounted_draft_workspaces_latest_source_message_id"):
+        op.drop_index(
+            "ix_mounted_draft_workspaces_latest_source_message_id",
+            table_name="mounted_draft_workspaces",
+        )
+    if _index_exists(bind, "mounted_draft_workspaces", "ix_mounted_draft_workspaces_active_work_item_id"):
+        op.drop_index(
+            "ix_mounted_draft_workspaces_active_work_item_id",
+            table_name="mounted_draft_workspaces",
+        )
+    if _column_exists(bind, "mounted_draft_workspaces", "latest_reply_message_id"):
+        op.drop_column("mounted_draft_workspaces", "latest_reply_message_id")
+    if _column_exists(bind, "mounted_draft_workspaces", "latest_source_message_id"):
+        op.drop_column("mounted_draft_workspaces", "latest_source_message_id")
+    if _column_exists(bind, "mounted_draft_workspaces", "active_work_item_id"):
+        op.drop_column("mounted_draft_workspaces", "active_work_item_id")

backend/app/config/ai_prompts.py

@@ -66,6 +66,37 @@ from .ai_prompt_registry import (
 # Deprecated compatibility aliases kept outside the registry surface.
 PROMPT_FEISHU_REQUIREMENT_ANALYSIS = PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION
 PROMPT_TASK_BREAKDOWN = PROMPT_DECOMPOSE_TASKS
+PMFLOW_SEMANTIC_CONTEXT_DECISION_PROMPT = """
+你是 PMFlow 的统一语义规划层。
+
+你的输入是系统整理好的 semantic context packet。你的职责不是执行写操作，而是：
+1. 判断用户这句话是否继续当前 topic。
+2. 判断当前 workflow 是否继续停留在 plan。
+3. 给出 draft patch 建议。
+4. 给出要回复给用户的内容。
+5. 明确 requested_actions 与是否需要确认。
+
+硬约束：
+- 只能依据 packet 中的事实、草稿、recent turns、mode card 判断。
+- 不得把推断包装成事实。
+- 不得请求 mode card 明确禁止的动作。
+- 输出必须是一个 JSON 对象，不要输出 Markdown。
+
+JSON schema:
+{
+  "topic_relation": "continue_current_topic | new_topic | uncertain",
+  "mode_decision": "stay_plan | ready_confirm | request_execute | answer_only",
+  "draft_patch": {},
+  "assistant_reply": "string",
+  "requires_confirmation": true,
+  "requested_actions": ["reply.compose"],
+  "confidence": 0.0,
+  "uncertainties": []
+}
+
+semantic_context_packet:
+{semantic_packet}
+""".strip()
 
 
 def build_default_role_prompt_bundle() -> dict:
@@ -198,6 +229,7 @@ __all__ = [
     "PROMPT_AI_PMO_REQUIREMENT_PLAN_QA",
     "PROMPT_EXTERNAL_TREND_QA",
     "PROMPT_INDUSTRY_TREND_QA",
+    "PMFLOW_SEMANTIC_CONTEXT_DECISION_PROMPT",
     "PROMPT_REGISTRY",
     "PROMPT_REGISTRY_MAP",
     "get_prompt_registry",

backend/app/config/ai_prompts_catalog/part_scenes_01.py

@@ -10,7 +10,9 @@ FEISHU_QA_RESPONSE_STYLE = _compose_prompt(
 2. 用正式、克制、面向协作的中文，像资深项目经理而不是聊天机器人。
 3. 如果能用数字、项目名、负责人、时间点表达，请直接写明。
 4. 若信息不足，必须明确说明缺少什么，最多列 3 项。
-5. 禁止自我介绍、寒暄、思考过程、数据库/模型/接口实现细节。
+5. 可使用 1-3 个轻量 emoji 做分段，但不要花哨，不要影响专业感。
+6. 飞书输出优先短段落和清晰换行；如果包含“结论/依据/下一步/信息不足”，要让这些段落一眼可扫读。
+7. 禁止自我介绍、寒暄、思考过程、数据库/模型/接口实现细节。
 """.strip(),
     TEXT_OUTPUT_GUARDRAIL,
 )
@@ -21,7 +23,7 @@ PROMPT_FEISHU_DIRECT_QA = _compose_prompt(
     """
 回答要求：
 1. 仅基于给定上下文回答，优先使用结构化数据。
-2. 如果问题涉及项目状态、进展、成员或任务，请优先给出可验证的事实和数字。
+2. 如果问题涉及项目状态、进展、成员或任务，请优先引用项目画像、检索证据和用户焦点里的可验证事实与数字。
 3. 如果问题涉及选择、建议或判断，直接给出明确结论和 1-2 条理由。
 4. 信息不足时，直接写“信息不足，缺少：...”并列出关键缺失项。
 5. 控制在 220 字以内，适合飞书消息阅读。
@@ -30,10 +32,11 @@ PROMPT_FEISHU_DIRECT_QA = _compose_prompt(
 
 当前项目ID：{project_id}
 问题：{question}
-System Snapshot：{system_snapshot}
+项目画像：{project_manifest}
-User Snapshot：{user_snapshot}
+系统摘要：{system_summary}
-最近会话：{conversation}
+用户焦点：{user_focus}
-检索上下文：{context_blocks}
+最近会话摘要：{conversation}
+检索证据：{context_blocks}
 """.strip(),
 )
 

backend/app/config/ai_prompts_catalog/part_scenes_05.py

@@ -52,6 +52,21 @@ PROMPT_AI_PMO_REQUIREMENT_PLAN_QA = _compose_prompt(
 6. 不能把未发生的创建、入库、执行写成已完成；如果用户没明确要求，不要推进执行。
 7. 如果上下文不够，只能说“我先帮你收敛需求”，并指出最关键缺口；不要编造系统已有模块或项目归属。
 8. 回复总长度尽量控制在 2-4 句，优先短句、口语化、像产品经理在继续追问，不要写成长文。
+9. 如果识别为“宽泛 IT 项目概念”，默认按五轮收敛：
+   - 第 1 轮：项目性质
+   - 第 2 轮：服务对象
+   - 第 3 轮：技术路线
+   - 第 4 轮：第一版范围
+   - 第 5 轮：推进方式
+10. 宽泛 IT 项目收敛时，每轮必须给 A/B/C 三个选项；每个选项都要写：
+   - 适合什么场景
+   - 推荐技术/能力
+   - 推荐理由
+   - 代价或限制
+11. 每轮 A/B/C 之后，必须补一句：
+   “当前我更推荐：X，原因：……”
+12. 宽泛 IT 项目收敛优先用项目管理口径，不要只给抽象概念词，不要让用户像在填表。
+13. 飞书输出允许使用轻量 emoji 做分段，但整体仍要专业、克制、易扫读。
 
 问题：{question}
 当前项目：{project_id}

backend/app/models/mounted_ai_runtime.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 
 from typing import Any, Optional
 
-from sqlalchemy import Boolean, Float, ForeignKey, Integer, Text, TIMESTAMP, UniqueConstraint, VARCHAR
+from sqlalchemy import BigInteger, Boolean, Float, ForeignKey, Integer, Text, TIMESTAMP, UniqueConstraint, VARCHAR
 from sqlalchemy.orm import Mapped, mapped_column
 
 from .base import Base
@@ -68,6 +68,9 @@ class MountedDraftWorkspace(Base):
     draft_payload: Mapped[Optional[Any]] = mapped_column(JSONB, nullable=True)
     required_confirmations: Mapped[Optional[Any]] = mapped_column(JSONB, nullable=True)
     last_ready_at: Mapped[Optional[TIMESTAMP]] = mapped_column(TIMESTAMP, nullable=True)
+    active_work_item_id: Mapped[Optional[int]] = mapped_column(BigInteger, nullable=True, index=True)
+    latest_source_message_id: Mapped[Optional[str]] = mapped_column(VARCHAR(128), nullable=True, index=True)
+    latest_reply_message_id: Mapped[Optional[str]] = mapped_column(VARCHAR(128), nullable=True, index=True)
     confirm_message_id: Mapped[Optional[str]] = mapped_column(VARCHAR(128), nullable=True, index=True)
 
 

backend/app/schemas/mounted_ai_runtime.py

@@ -150,6 +150,20 @@ class MountedAIRunListPayload(BaseModel):
     total: int = 0
 
 
+class MountedAIDraftItem(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    id: int
+    topic_workspace_id: int
+    state: str
+    draft_version: int
+    requirement_card: Optional[Any] = None
+    draft_payload: Optional[Any] = None
+    required_confirmations: Optional[Any] = None
+    last_ready_at: Optional[str] = None
+    confirm_message_id: Optional[str] = None
+
+
 class MountedAIAgentTraceItem(BaseModel):
     model_config = ConfigDict(extra="ignore")
 
@@ -168,10 +182,16 @@ class MountedAIRunDetailPayload(BaseModel):
     model_config = ConfigDict(extra="ignore")
 
     item: MountedAIRunItem
+    topic: Optional[MountedAITopicItem] = None
+    draft: Optional[MountedAIDraftItem] = None
     agent_traces: list[MountedAIAgentTraceItem] = Field(default_factory=list)
     context_layers_hit: Optional[Any] = None
     tool_calls: Optional[Any] = None
     execution_outcome: Optional[Any] = None
+    semantic_packet_summary: Optional[Any] = None
+    semantic_decision: Optional[Any] = None
+    semantic_gate_result: Optional[Any] = None
+    semantic_diff: Optional[Any] = None
 
 
 class MountedAICutoverStatusPayload(BaseModel):

backend/app/services/adaptive_rag_service/mixin_deep_guard_answer.py

@@ -92,6 +92,178 @@ class AdaptiveRAGDeepGuardAnswerMixin:
                 kept.append(text)
         return "\n".join(kept) if kept else "无"
 
+    @staticmethod
+    def _truncate_compact_text(text: Optional[str], *, limit: int, empty: str = "无") -> str:
+        raw = str(text or "").strip()
+        if not raw:
+            return empty
+        if len(raw) <= max(1, int(limit)):
+            return raw
+        clipped = raw[: max(1, int(limit) - 8)].rstrip()
+        return f"{clipped}...(截断)"
+
+    @staticmethod
+    def _split_context_blocks(context_blocks_text: str) -> list[str]:
+        raw = str(context_blocks_text or "").strip()
+        if not raw or raw == "无":
+            return []
+        return [
+            str(block).strip()
+            for block in re.split(r"\n(?=\[(?:\d+|M)\])", raw)
+            if str(block).strip()
+        ]
+
+    @classmethod
+    def _extract_manifest_block(cls, context_blocks_text: str) -> str:
+        for block in cls._split_context_blocks(context_blocks_text):
+            if not block.startswith("[M]"):
+                continue
+            lines = [str(line).strip() for line in block.splitlines()]
+            payload = "\n".join(line for line in lines[1:] if line).strip()
+            return cls._truncate_compact_text(payload, limit=260)
+        return "无"
+
+    @classmethod
+    def _build_feishu_direct_project_manifest(
+        cls,
+        *,
+        project_id: Optional[int],
+        context_blocks_text: str,
+        user_snapshot: Optional[dict[str, Any]],
+    ) -> str:
+        manifest_text = cls._extract_manifest_block(context_blocks_text)
+        if manifest_text != "无":
+            return manifest_text
+        bits: list[str] = []
+        if project_id is not None:
+            bits.append(f"当前项目ID {int(project_id)}")
+        projects = user_snapshot.get("projects") if isinstance(user_snapshot, dict) else None
+        if isinstance(projects, list) and projects:
+            project_names = [
+                str(item.get("title") or item.get("name") or item.get("id") or "").strip()
+                for item in projects[:3]
+                if isinstance(item, dict)
+            ]
+            project_names = [name for name in project_names if name]
+            bits.append(f"关联项目 {len(projects)} 个")
+            if project_names:
+                bits.append(f"重点项目：{'、'.join(project_names)}")
+        return cls._truncate_compact_text("；".join(bits), limit=260)
+
+    @classmethod
+    def _build_feishu_direct_system_summary(cls, system_snapshot: Optional[dict[str, Any]]) -> str:
+        system_data = system_snapshot if isinstance(system_snapshot, dict) else {}
+        assistant = system_data.get("assistant_profile") if isinstance(system_data.get("assistant_profile"), dict) else {}
+        platform = system_data.get("platform_profile") if isinstance(system_data.get("platform_profile"), dict) else {}
+        backend = platform.get("backend") if isinstance(platform.get("backend"), dict) else {}
+        module_counts = backend.get("module_counts") if isinstance(backend.get("module_counts"), dict) else {}
+        feature_catalog = platform.get("feature_catalog") if isinstance(platform.get("feature_catalog"), list) else []
+
+        bits: list[str] = []
+        assistant_name = str(assistant.get("name") or "").strip()
+        assistant_identity = str(assistant.get("identity") or "").strip()
+        if assistant_name or assistant_identity:
+            identity_text = " / ".join(part for part in (assistant_name, assistant_identity) if part)
+            bits.append(f"助手：{identity_text}")
+        if module_counts:
+            top_modules = [
+                f"{str(key)} {int(value)}"
+                for key, value in list(module_counts.items())[:4]
+                if value is not None
+            ]
+            if top_modules:
+                bits.append(f"模块统计：{'、'.join(top_modules)}")
+        if feature_catalog:
+            feature_names = [
+                str(item.get("name") or item.get("route") or "").strip()
+                for item in feature_catalog[:4]
+                if isinstance(item, dict)
+            ]
+            feature_names = [name for name in feature_names if name]
+            bits.append(f"功能目录 {len(feature_catalog)} 个")
+            if feature_names:
+                bits.append(f"高频能力：{'、'.join(feature_names)}")
+        return cls._truncate_compact_text("；".join(bits), limit=320)
+
+    @classmethod
+    def _build_feishu_direct_user_focus(cls, user_snapshot: Optional[dict[str, Any]]) -> str:
+        user_data = user_snapshot if isinstance(user_snapshot, dict) else {}
+        current_user = (
+            user_data.get("current_user")
+            if isinstance(user_data.get("current_user"), dict)
+            else user_data.get("user_profile")
+            if isinstance(user_data.get("user_profile"), dict)
+            else {}
+        )
+        projects = user_data.get("projects") if isinstance(user_data.get("projects"), list) else []
+        tasks = user_data.get("tasks") if isinstance(user_data.get("tasks"), list) else []
+        bits: list[str] = []
+        current_user_name = str(
+            current_user.get("name")
+            or current_user.get("display_name")
+            or current_user.get("username")
+            or ""
+        ).strip()
+        if current_user_name:
+            bits.append(f"当前用户：{current_user_name}")
+        if projects:
+            project_names = [
+                str(item.get("title") or item.get("name") or item.get("id") or "").strip()
+                for item in projects[:3]
+                if isinstance(item, dict)
+            ]
+            project_names = [name for name in project_names if name]
+            focus_text = f"关联项目 {len(projects)} 个"
+            if project_names:
+                focus_text = f"{focus_text}（{', '.join(project_names)}）"
+            bits.append(focus_text)
+        if tasks:
+            task_names = [
+                str(item.get("title") or item.get("name") or item.get("id") or "").strip()
+                for item in tasks[:3]
+                if isinstance(item, dict)
+            ]
+            task_names = [name for name in task_names if name]
+            task_text = f"待关注任务 {len(tasks)} 个"
+            if task_names:
+                task_text = f"{task_text}（{', '.join(task_names)}）"
+            bits.append(task_text)
+        return cls._truncate_compact_text("；".join(bits), limit=320)
+
+    @classmethod
+    def _compact_feishu_direct_conversation(cls, conversation_text: str) -> str:
+        raw = str(conversation_text or "").strip()
+        if not raw or raw == "无":
+            return "无"
+        blocks = [
+            cls._truncate_compact_text(block, limit=180, empty="")
+            for block in re.split(r"\n(?=Q:)", raw)
+            if str(block).strip()
+        ]
+        compact = "\n".join(block for block in blocks[-3:] if block)
+        return cls._truncate_compact_text(compact, limit=520)
+
+    @classmethod
+    def _compact_feishu_direct_context_blocks(cls, context_blocks_text: str) -> str:
+        noise_markers = ("项目列表", "历史项目详情", "assistant_profile", "platform_profile")
+        kept: list[str] = []
+        fallback: Optional[str] = None
+        for block in cls._split_context_blocks(context_blocks_text):
+            if block.startswith("[M]"):
+                continue
+            compact_block = cls._truncate_compact_text(block, limit=220, empty="")
+            if not compact_block:
+                continue
+            fallback = fallback or compact_block
+            if any(marker in compact_block for marker in noise_markers):
+                continue
+            kept.append(compact_block)
+            if len(kept) >= 2:
+                break
+        if not kept and fallback:
+            kept.append(fallback)
+        return cls._truncate_compact_text("\n".join(kept), limit=520)
+
     @staticmethod
     def _is_ai_pmo_process_entry(asked_via: Optional[str]) -> bool:
         channel = str(asked_via or "").strip().lower()
@@ -444,6 +616,15 @@ class AdaptiveRAGDeepGuardAnswerMixin:
 
         normalized_asked_via = str(asked_via or "").strip().lower()
         if normalized_asked_via in {"feishu", "lark", "feishu_group", "feishu_dm"}:
+            project_manifest_text = self._build_feishu_direct_project_manifest(
+                project_id=project_id,
+                context_blocks_text=context_blocks_text,
+                user_snapshot=user_snapshot,
+            )
+            system_summary_text = self._build_feishu_direct_system_summary(system_snapshot)
+            user_focus_text = self._build_feishu_direct_user_focus(user_snapshot)
+            compact_conversation_text = self._compact_feishu_direct_conversation(conversation_text)
+            compact_context_blocks_text = self._compact_feishu_direct_context_blocks(context_blocks_text)
             scene_key, prompt = (
                 "feishu_direct",
                 await self._render_scene_prompt(
@@ -451,10 +632,11 @@ class AdaptiveRAGDeepGuardAnswerMixin:
                     fallback_template=PROMPT_FEISHU_DIRECT_QA,
                     project_id=project_id if project_id is not None else "unknown",
                     question=question,
-                    system_snapshot=system_snapshot_text or "{}",
+                    project_manifest=project_manifest_text,
-                    user_snapshot=user_snapshot_text or "{}",
+                    system_summary=system_summary_text,
-                    conversation=conversation_text or "无",
+                    user_focus=user_focus_text,
-                    context_blocks=context_blocks_text or "无",
+                    conversation=compact_conversation_text,
+                    context_blocks=compact_context_blocks_text,
                 ),
             )
             return self._apply_consultant_mode_preference(

backend/app/services/ai_pmo/ai_pmo_kernel/ai_pmo_typed_executor_common.py

@@ -70,6 +70,7 @@ from ..ai_pmo_intent import (
     ROUTE_ACTION_ACK,
     ROUTE_ACTION_CLOSE,
     ROUTE_ACTION_DISCOVER,
+    ROUTE_PROJECT_CREATE,
     ROUTE_PROJECT_SIGNAL_CREATE,
     ROUTE_PROJECT_SIGNAL_DISCOVER,
     ROUTE_PROJECT_UPDATE,

backend/app/services/ai_pmo/ai_pmo_kernel/mixins/ingress/ai_pmo_kernel_mixins_ingress_render.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 from .... import ai_pmo_kernel_common_core as _common
-from ....workflow_semantic_planner import WorkflowSemanticPlanner
 
 for _name, _value in vars(_common).items():
     if not _name.startswith("__"):
@@ -11,6 +10,42 @@ for _name, _value in vars(_common).items():
 
 
 class KernelIngressRenderMixin:
+    @staticmethod
+    def _build_semantic_bundle_compat(
+        *,
+        intent: CanonicalIntent,
+        workflow_route: Optional[str],
+        source_system: Optional[str],
+        message_text: str,
+    ) -> dict[str, Any]:
+        metadata = intent.metadata if isinstance(intent.metadata, dict) else {}
+        diagnostics: dict[str, Any] = {}
+        for key in (
+            "message_intent",
+            "review_framework",
+            "consultant_mode_preference",
+            "force_thinking_mode",
+            "advise_subtype",
+            "topic_switch",
+        ):
+            value = metadata.get(key)
+            if value not in (None, ""):
+                diagnostics[key] = value
+        if str(intent.name or "").strip():
+            diagnostics["intent_name"] = str(intent.name or "").strip()
+        if str(workflow_route or "").strip():
+            diagnostics["workflow_route"] = str(workflow_route or "").strip()
+        primary_workflow = "query_flow" if str(workflow_route or "").strip().startswith("query.") else "action_flow"
+        return {
+            "bundle_id": None,
+            "semantic_version": "compat-direct-metadata-v1",
+            "source_system": str(source_system or "unknown"),
+            "primary_workflow": primary_workflow,
+            "message_gate": "needs_confirmation" if bool(metadata.get("requires_confirmation")) else "auto_execute",
+            "diagnostics": diagnostics,
+            "raw_message_excerpt": re.sub(r"\s+", " ", str(message_text or "")).strip()[:240],
+        }
+
     def _build_capture_result(
         self,
         *,
@@ -94,18 +129,16 @@ class KernelIngressRenderMixin:
             else None
         )
         if not isinstance(semantic_bundle, dict):
-            semantic_bundle = WorkflowSemanticPlanner.plan_from_intent(
+            semantic_bundle = self._build_semantic_bundle_compat(
-                intent_name=str(intent.name or ""),
+                intent=intent,
-                message_text=str(getattr(work_item, "raw_message", "") or direct_reply_text or intent.signal_summary or ""),
+                workflow_route=workflow_route,
                 source_system=str(getattr(work_item, "source_system", "") or "unknown"),
-                workflow_route=str(workflow_route or ""),
+                message_text=str(getattr(work_item, "raw_message", "") or direct_reply_text or intent.signal_summary or ""),
-                project_id=_safe_int(work_item.project_id),
+            )
-                metadata=intent.metadata if isinstance(intent.metadata, dict) else {},
-            ).to_dict()
         result["semantic_bundle"] = semantic_bundle
         result["semantic_bundle_id"] = str(semantic_bundle.get("bundle_id") or "").strip() or None
         result["semantic_version"] = str(semantic_bundle.get("semantic_version") or "").strip() or None
-        result["semantic_source"] = "workflow_semantic_planner"
+        result["semantic_source"] = "direct_metadata_compat"
         if str(render.get("template_key") or "").strip().lower() == "draft_pending_confirm":
             payload = render.get("payload") if isinstance(render.get("payload"), dict) else {}
             result["draft_version"] = _safe_int(payload.get("draft_version")) or _safe_int(getattr(work_item, "draft_version", None))

backend/app/services/ai_pmo/ai_pmo_kernel/mixins/intent/ai_pmo_kernel_mixins_intent_llm.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 from .... import ai_pmo_kernel_common_core as _common
-from ....workflow_semantic_planner import WorkflowSemanticPlanner
 
 for _name, _value in vars(_common).items():
     if not _name.startswith("__"):
@@ -199,26 +198,6 @@ class KernelIntentLLMMixin:
                         if structured_route is not None
                         else bool(payload.get("requires_confirmation"))
                     ),
-                    "semantic_bundle": WorkflowSemanticPlanner.plan_from_intent(
-                        intent_name=intent_name,
-                        message_text=message_text,
-                        source_system="ai_pmo_ingress",
-                        workflow_route=(
-                            structured_route.key
-                            if structured_route is not None
-                            else LEGACY_INTENT_ROUTE_MAP.get(intent_name, ROUTE_QUERY_ANSWER)
-                        ),
-                        project_id=project_id,
-                        metadata={
-                            **payload,
-                            "confidence": confidence,
-                            "requires_confirmation": (
-                                structured_route.requires_confirmation
-                                if structured_route is not None
-                                else bool(payload.get("requires_confirmation"))
-                            ),
-                        },
-                    ).to_dict(),
                 },
             )
 

backend/app/services/ai_pmo/ai_pmo_kernel/mixins/intent/ai_pmo_kernel_mixins_intent_resolve.py

@@ -11,6 +11,26 @@ for _name, _value in vars(_common).items():
 
 
 class KernelIntentResolveMixin:
+    _QUERY_FOLLOWUP_TOKENS = (
+        "需求",
+        "mvp",
+        "方案",
+        "对应",
+        "这个",
+        "它",
+        "给我",
+        "补充",
+        "展开",
+        "细化",
+        "重答",
+        "重新回答",
+        "高级模型",
+        "更强模型",
+        "分析",
+        "评审",
+        "怎么做",
+    )
+
     @staticmethod
     def _within_recent_window(last_active_at: Optional[datetime], *, window_seconds: int) -> bool:
         if not isinstance(last_active_at, datetime):
@@ -18,6 +38,13 @@ class KernelIntentResolveMixin:
         delta = _utc_now_naive() - last_active_at
         return 0 <= delta.total_seconds() <= float(window_seconds)
 
+    @classmethod
+    def _looks_like_query_scene_followup(cls, message_text: str) -> bool:
+        normalized = re.sub(r"\s+", "", str(message_text or "")).lower()
+        if not normalized:
+            return False
+        return any(token in normalized for token in cls._QUERY_FOLLOWUP_TOKENS)
+
     @classmethod
     def _should_inherit_recent_feishu_session_context(
         cls,
@@ -27,6 +54,8 @@ class KernelIntentResolveMixin:
     ) -> bool:
         if session is None:
             return False
+        if WorkflowSemanticPlanner.looks_like_explicit_new_requirement_topic(message_text):
+            return False
         sender = str(getattr(session, "sender", "") or "").strip().lower()
         if not sender.startswith("feishu:"):
             return False
@@ -37,28 +66,7 @@ class KernelIntentResolveMixin:
             return False
         if not str(getattr(session, "last_intent", "") or "").strip():
             return False
-        if WorkflowSemanticPlanner._derive_query_scene_metadata(message_text):
+        return cls._looks_like_query_scene_followup(message_text)
-            return True
-        normalized = re.sub(r"\s+", "", str(message_text or "")).lower()
-        if not normalized:
-            return False
-        followup_tokens = (
-            "需求",
-            "mvp",
-            "方案",
-            "对应",
-            "这个",
-            "它",
-            "给我",
-            "补充",
-            "展开",
-            "细化",
-            "重答",
-            "重新回答",
-            "高级模型",
-            "更强模型",
-        )
-        return any(token in normalized for token in followup_tokens)
 
     async def _resolve_intent(
         self,
@@ -101,29 +109,6 @@ class KernelIntentResolveMixin:
             )
             return inherited, "session_inherited"
 
-        if WorkflowSemanticPlanner.looks_like_signal_source_request(message_text):
-            project_id = _safe_int(world_model.get("resolved_project_id"))
-            if project_id is None and inherit_session_context and session is not None:
-                project_id = _safe_int(getattr(session, "current_project_id", None))
-            return self._build_canonical_intent(
-                intent_name=INTENT_SIGNAL_SOURCE_ONBOARD,
-                message_text=message_text,
-                project_id=project_id,
-                confidence=0.92,
-                metadata={
-                    "context_source": "semantic_signal_source_locked",
-                    "source_request": True,
-                    "semantic_bundle": WorkflowSemanticPlanner.plan_from_intent(
-                        intent_name=INTENT_SIGNAL_SOURCE_ONBOARD,
-                        message_text=message_text,
-                        source_system="ai_pmo_ingress",
-                        workflow_route=ROUTE_SIGNAL_SOURCE_ONBOARD,
-                        project_id=project_id,
-                        metadata={"confidence": 0.92, "requires_confirmation": False},
-                    ).to_dict(),
-                },
-            ), "rule_signal_source_onboard"
-
         # 模型唯一入口：不再使用规则/正则意图分流。
         if not self._intent_llm_enabled():
             raise RuntimeError("intent_model_only_mode_requires_llm_enabled")

backend/app/services/ai_pmo/ai_pmo_kernel/mixins/world_model/ai_pmo_kernel_mixins_world_model_session.py

@@ -110,6 +110,7 @@ class KernelWorldModelSessionMixin:
         source_message_id: Optional[str] = None,
         thread_context: Optional[dict[str, Any]] = None,
         current_project_id: Optional[int],
+        clear_current_project_id: bool = False,
         last_intent: Optional[str],
         last_message_text: Optional[str],
     ) -> None:
@@ -122,7 +123,9 @@ class KernelWorldModelSessionMixin:
             source_message_id=source_message_id,
             thread_context=thread_context,
         )
-        if current_project_id is not None:
+        if clear_current_project_id:
+            row.current_project_id = None
+        elif current_project_id is not None:
             row.current_project_id = int(current_project_id)
         row.last_intent = (str(last_intent or "").strip()[:64] or None)
         trimmed_message = str(last_message_text or "").strip()

backend/app/services/ai_pmo/ai_pmo_kernel_ingress_flow/mixin_auto.py

@@ -209,6 +209,30 @@ class KernelIngressFlowAutoMixin:
                 channel_ref=channel_ref,
             )
             log_perf_fn("TypedExecutor.execute", stage_started_at)
+            if self._should_promote_requirement_plan_draft(
+                intent=intent,
+                execution_result=execution_result,
+            ):
+                stage_started_at = time.perf_counter()
+                capture_result = await self._promote_requirement_plan_draft_confirmation(
+                    work_item=work_item,
+                    intent=intent,
+                    execution_result=execution_result,
+                    world_model=world_model,
+                    decision_payload=decision_payload,
+                    decision_trace=decision_trace,
+                    decision_row=decision_row,
+                    source_system=source_system,
+                    source_message_id=source_message_id,
+                    message_text=message_text,
+                    sender=sender,
+                    ingest_event=ingest_event,
+                    started=started,
+                    session=session,
+                    resolved_project_for_session=resolved_project_for_session,
+                )
+                log_perf_fn("_promote_requirement_plan_draft_confirmation", stage_started_at)
+                return capture_result
             verification = await self._verify_execution(intent=intent, execution_result=execution_result, world_model=world_model)
         if isinstance(execution_result, dict):
             execution_result.setdefault("decision_trace", decision_trace)
@@ -405,6 +429,262 @@ class KernelIngressFlowAutoMixin:
             return str(getattr(workflow_route, "action", "") or "") == ROUTE_ACTION_CREATE
         return True
 
+    @staticmethod
+    def _should_promote_requirement_plan_draft(
+        *,
+        intent: CanonicalIntent,
+        execution_result: Any,
+    ) -> bool:
+        if intent.name != INTENT_QUERY or not isinstance(execution_result, dict):
+            return False
+        artifact = execution_result.get("draft_artifact")
+        if not isinstance(artifact, dict):
+            return False
+        return bool(artifact.get("promote_to_confirmation"))
+
+    async def _promote_requirement_plan_draft_confirmation(
+        self,
+        *,
+        work_item: Any,
+        intent: CanonicalIntent,
+        execution_result: dict[str, Any],
+        world_model: dict[str, Any],
+        decision_payload: dict[str, Any],
+        decision_trace: dict[str, Any],
+        decision_row: Any,
+        source_system: str,
+        source_message_id: str,
+        message_text: str,
+        sender: str,
+        ingest_event: Any,
+        started: float,
+        session: ConversationSession,
+        resolved_project_for_session: Optional[int],
+    ) -> dict[str, Any]:
+        from ....models import MountedDraftWorkspace, MountedTopicWorkspace
+
+        artifact = execution_result.get("draft_artifact") if isinstance(execution_result, dict) else {}
+        draft_workspace_id = _safe_int((artifact or {}).get("draft_workspace_id"))
+        if draft_workspace_id is None:
+            return {
+                "handled": False,
+                "status": "failed_safe",
+                "error": "requirement_plan_draft_missing_workspace",
+            }
+
+        draft_row = (
+            await self.db.execute(
+                select(MountedDraftWorkspace).where(
+                    and_(
+                        MountedDraftWorkspace.tenant_id == int(self.tenant_id),
+                        MountedDraftWorkspace.id == int(draft_workspace_id),
+                    )
+                )
+            )
+        ).scalar_one_or_none()
+        if draft_row is None:
+            return {
+                "handled": False,
+                "status": "failed_safe",
+                "error": "requirement_plan_draft_not_found",
+            }
+
+        topic_row = (
+            await self.db.execute(
+                select(MountedTopicWorkspace).where(
+                    and_(
+                        MountedTopicWorkspace.tenant_id == int(self.tenant_id),
+                        MountedTopicWorkspace.id == int(draft_row.topic_workspace_id),
+                    )
+                )
+            )
+        ).scalar_one_or_none()
+        if topic_row is None:
+            return {
+                "handled": False,
+                "status": "failed_safe",
+                "error": "requirement_plan_topic_not_found",
+            }
+
+        draft_payload = draft_row.draft_payload if isinstance(draft_row.draft_payload, dict) else {}
+        if not draft_payload:
+            draft_payload = (
+                artifact.get("draft_payload")
+                if isinstance(artifact.get("draft_payload"), dict)
+                else {}
+            )
+        draft_text = str((artifact or {}).get("draft_text") or draft_payload.get("draft_text") or execution_result.get("answer") or "").strip()
+        draft_version = _safe_int((artifact or {}).get("draft_version")) or int(draft_row.draft_version or 0) or 1
+
+        work_item.intent = ROUTE_PROJECT_CREATE
+        work_item.state = STATE_DRAFT_PENDING_CONFIRM
+        work_item.draft_content = draft_payload
+        work_item.draft_version = int(draft_version)
+        work_item.draft_expires_at = None
+        work_item.confirm_message_id = None
+        work_item.execution_result = {
+            **(execution_result if isinstance(execution_result, dict) else {}),
+            "status": "draft_pending_confirm",
+            "workflow_route": ROUTE_PROJECT_CREATE,
+            "workflow_action": ROUTE_ACTION_CREATE,
+            "source_workflow_route": str((artifact or {}).get("source_workflow_route") or ROUTE_QUERY_ANSWER),
+            "draft": draft_payload,
+            "draft_version": int(draft_version),
+            "draft_artifact": {
+                **(artifact if isinstance(artifact, dict) else {}),
+                "active_work_item_id": int(work_item.id),
+            },
+        }
+        work_item.reply_template_key = "draft_pending_confirm"
+
+        draft_row.state = STATE_DRAFT_PENDING_CONFIRM
+        draft_row.draft_version = int(draft_version)
+        draft_row.draft_payload = draft_payload
+        draft_row.active_work_item_id = int(work_item.id)
+        draft_row.latest_source_message_id = str(source_message_id or "").strip()[:128] or None
+        draft_row.latest_reply_message_id = None
+        draft_row.confirm_message_id = None
+        draft_row.last_ready_at = _utc_now_naive()
+
+        topic_row.mode = "plan"
+        topic_row.status = "active"
+        topic_row.plan_status = STATE_DRAFT_PENDING_CONFIRM
+        topic_row.pending_actions = ["confirm_requirement_draft"]
+        topic_row.open_questions = []
+        topic_row.readiness_score = 1.0
+        topic_row.last_message_id = str(source_message_id or "").strip()[:128] or topic_row.last_message_id
+        topic_row.metadata_json = {
+            **(topic_row.metadata_json if isinstance(topic_row.metadata_json, dict) else {}),
+            "plan_mode": True,
+            "plan_topic_action": "draft_pending_confirm",
+            "active_draft_work_item_id": int(work_item.id),
+            "active_draft_version": int(draft_version),
+        }
+
+        verification = {"verified": False, "issues": [STATE_DRAFT_PENDING_CONFIRM]}
+        work_item.review_summary = await self.review_service.review_work_item(
+            work_item=work_item,
+            intent_name=INTENT_PROJECT_CREATE,
+            execution_result=work_item.execution_result,
+            world_model=world_model,
+            verification=verification,
+            simulation=decision_payload["simulation"],
+        )
+        await self._maybe_emit_risk_cadence_snapshot(
+            project_id=_safe_int(work_item.project_id),
+            review_summary=work_item.review_summary,
+        )
+
+        decision_row.execution_result = work_item.execution_result
+        decision_row.final_outcome = STATE_DRAFT_PENDING_CONFIRM
+        operation_code_for_log = "project.create"
+        await self._upsert_operation_execution_ledger(
+            work_item_id=int(work_item.id),
+            operation_code=operation_code_for_log,
+            source_message_id=source_message_id,
+            project_id=_safe_int(work_item.project_id),
+            status=self._normalize_execution_ledger_status(
+                work_item_state=STATE_DRAFT_PENDING_CONFIRM,
+                execution_status=str(work_item.execution_result.get("status") or ""),
+            ),
+            payload={
+                "work_item_id": int(work_item.id),
+                "workflow_route": ROUTE_PROJECT_CREATE,
+                "workflow_action": ROUTE_ACTION_CREATE,
+                "source_system": source_system,
+                "source_message_id": source_message_id,
+                "draft_artifact": artifact if isinstance(artifact, dict) else {},
+            },
+            execution_result={
+                **work_item.execution_result,
+                "work_item_state": STATE_DRAFT_PENDING_CONFIRM,
+            },
+        )
+
+        render = {
+            "receipt_phase": RECEIPT_PHASE_PENDING,
+            "template_key": "draft_pending_confirm",
+            "payload": {
+                "intent": INTENT_PROJECT_CREATE,
+                "draft_version": int(draft_version),
+                "draft_expires_at": None,
+            },
+            "text": draft_text or "已生成草稿，请确认后入库。",
+        }
+        capture_intent = CanonicalIntent(
+            name=INTENT_PROJECT_CREATE,
+            confidence=max(float(intent.confidence or 0.0), 0.9),
+            project_id=_safe_int(work_item.project_id) or resolved_project_for_session,
+            signal_title="",
+            signal_summary=str(message_text or "")[:300],
+            metadata={
+                **(intent.metadata if isinstance(intent.metadata, dict) else {}),
+                "workflow_route": ROUTE_PROJECT_CREATE,
+                "workflow_family": ROUTE_FAMILY_PROJECT_CREATE,
+                "workflow_action": ROUTE_ACTION_CREATE,
+                "requires_confirmation": True,
+                "resumed_from": ROUTE_QUERY_ANSWER,
+            },
+        )
+
+        await self._project_draft_pending_confirm(
+            work_item=work_item,
+            message_text=message_text,
+            draft_text=str(render["text"]),
+            intent=capture_intent,
+        )
+
+        direct_reply_required = await self._claim_reply_receipt(
+            work_item_id=int(work_item.id),
+            source_system=source_system,
+            source_message_id=source_message_id,
+            receipt_phase=str(render["receipt_phase"]),
+            template_key=str(render["template_key"]),
+            payload=render["payload"],
+        )
+        ingest_event.status = "done"
+        ingest_event.processed_at = _utc_now_naive()
+        await self._write_processing_log(
+            source_message_id=source_message_id,
+            work_item=work_item,
+            message_text=message_text,
+            route=f"ai_pmo.{STATE_DRAFT_PENDING_CONFIRM}",
+            operation_code=operation_code_for_log,
+            operation_result=work_item.execution_result,
+            processing_status="success",
+            duration_ms=int((time.perf_counter() - started) * 1000),
+        )
+        final_project_id = _safe_int(work_item.project_id) or resolved_project_for_session
+        await self._update_session(
+            tenant_id=self.tenant_id,
+            sender=sender,
+            session=session,
+            current_project_id=final_project_id,
+            last_intent=STATE_DRAFT_PENDING_CONFIRM,
+            last_message_text=message_text[:200],
+        )
+        try:
+            await self.db.commit()
+        except Exception as exc:
+            await self._persist_kernel_failure_with_fresh_session(
+                work_item_id=_safe_int(getattr(work_item, "id", None)),
+                ingest_event_id=_safe_int(getattr(ingest_event, "id", None)),
+                error_message=str(exc),
+                source_system=source_system,
+                source_message_id=source_message_id,
+            )
+            raise
+
+        return self._build_capture_result(
+            work_item=work_item,
+            intent=capture_intent,
+            execution_result=work_item.execution_result,
+            render=render,
+            direct_reply_required=direct_reply_required,
+            status="captured",
+            classification="command",
+        )
+
     @staticmethod
     def _build_capacity_warning_text(
         *,

backend/app/services/ai_pmo/ai_pmo_kernel_ingress_flow/mixin_entry.py

@@ -2,6 +2,11 @@ from __future__ import annotations
 
 from .shared import *
 from app.utils.thread_context import build_feishu_conversation_scope_key
+from app.services.ai_pmo.semantic_context import (
+    SemanticContextAssembler,
+    SemanticDecisionGatekeeper,
+)
+from app.services.ai_pmo.semantic_context.semantic_decision_service import SemanticDecisionService
 from app.services.ai_pmo.workflow_semantic_planner import WorkflowSemanticPlanner
 from app.services.ai_pmo.workflow_semantic_planner.constants import (
     QUERY_SCENE_BUSINESS_REVIEW,
@@ -27,6 +32,47 @@ class KernelIngressFlowEntryMixin:
         QUERY_SCENE_CORRECTION_ESCALATION,
         QUERY_SCENE_REQUIREMENT_PLAN,
     })
+    _QUERY_SCENE_TOKEN_RULES = (
+        (
+            QUERY_SCENE_CORRECTION_ESCALATION,
+            {
+                "consultant_mode_preference": "challenge",
+                "force_thinking_mode": "deep",
+            },
+            ("不对", "错了", "答错", "纠正", "更正", "重新回答", "重答", "高级模型", "更强模型"),
+        ),
+        (
+            QUERY_SCENE_BUSINESS_REVIEW,
+            {
+                "review_framework": "five_lenses",
+                "consultant_mode_preference": "clarify",
+            },
+            ("分析", "拆解", "评审", "评估", "复盘", "诊断", "怎么做", "mvp", "需求"),
+        ),
+        (
+            QUERY_SCENE_BUSINESS_REVIEW,
+            {
+                "consultant_mode_preference": "advise",
+                "advise_subtype": "assignee",
+            },
+            ("谁来做", "谁负责", "安排谁", "推荐谁", "负责人"),
+        ),
+        (
+            QUERY_SCENE_REQUIREMENT_PLAN,
+            {
+                "consultant_mode_preference": "clarify",
+                "topic_switch": "new",
+            },
+            ("新需求", "新项目", "做个", "想做个", "准备做", "创建需求"),
+        ),
+        (
+            QUERY_SCENE_REQUIREMENT_PLAN,
+            {
+                "consultant_mode_preference": "clarify",
+            },
+            ("需求", "方案", "范围", "排期", "补充", "细化", "展开", "mvp"),
+        ),
+    )
 
     @classmethod
     def _extract_route_scene_metadata(cls, route_metadata: Optional[dict[str, Any]]) -> dict[str, str]:
@@ -36,16 +82,26 @@ class KernelIngressFlowEntryMixin:
             value = metadata.get(key)
             if value not in (None, ""):
                 scene_metadata[key] = str(value).strip()
-        semantic_diagnostics = metadata.get("semantic_bundle")
-        if isinstance(semantic_diagnostics, dict):
-            semantic_diagnostics = semantic_diagnostics.get("diagnostics")
-        if isinstance(semantic_diagnostics, dict):
-            for key in cls._RAG_QUERY_SCENE_KEYS:
-                value = semantic_diagnostics.get(key)
-                if value not in (None, "") and key not in scene_metadata:
-                    scene_metadata[key] = str(value).strip()
         return scene_metadata
 
+    @classmethod
+    def _derive_query_scene_metadata(cls, text: str) -> dict[str, Any]:
+        normalized = re.sub(r"\s+", "", str(text or "")).lower()
+        if not normalized:
+            return {}
+        if any(token in normalized for token in ("最近怎么样", "在吗", "你好", "哈哈", "hi", "hello")):
+            return {
+                "message_intent": "casual_chat",
+                "consultant_mode_preference": "brainstorm",
+            }
+        for message_intent, extra_fields, tokens in cls._QUERY_SCENE_TOKEN_RULES:
+            if any(token in normalized for token in tokens):
+                return {
+                    "message_intent": message_intent,
+                    **extra_fields,
+                }
+        return {}
+
     @classmethod
     def _looks_like_mixed_candidate(cls, *, text: str) -> bool:
         normalized = re.sub(r"\s+", "", str(text or "")).lower()
@@ -73,30 +129,12 @@ class KernelIngressFlowEntryMixin:
         semantic_bundle: Optional[dict[str, Any]],
     ) -> dict[str, Any]:
         context = rag_request_context if isinstance(rag_request_context, dict) else {}
-        semantic_diagnostics = (
+        derived = cls._derive_query_scene_metadata(message_text)
-            semantic_bundle.get("diagnostics")
-            if isinstance(semantic_bundle, dict) and isinstance(semantic_bundle.get("diagnostics"), dict)
-            else {}
-        )
-        semantic_primary_workflow = (
-            str(semantic_bundle.get("primary_workflow") or "").strip().lower()
-            if isinstance(semantic_bundle, dict)
-            else ""
-        )
-        derived = WorkflowSemanticPlanner._derive_query_scene_metadata(message_text)
         scene_metadata: dict[str, Any] = {
             key: value
             for key, value in derived.items()
             if key in cls._RAG_QUERY_SCENE_KEYS and value not in (None, "")
         }
-        if str(source_system or "").strip().lower() == "feishu" and semantic_primary_workflow == "query_flow":
-            for key in cls._RAG_QUERY_SCENE_KEYS:
-                value = semantic_diagnostics.get(key)
-                if value in (None, ""):
-                    continue
-                text = str(value).strip()
-                if text:
-                    scene_metadata[key] = text.lower() if key != "review_framework" else text
         for key in cls._RAG_QUERY_SCENE_KEYS:
             value = context.get(key)
             if value in (None, ""):
@@ -115,20 +153,14 @@ class KernelIngressFlowEntryMixin:
         semantic_bundle: Optional[dict[str, Any]],
         scene_metadata: dict[str, Any],
     ) -> bool:
+        _ = semantic_bundle
         message_intent = str(scene_metadata.get("message_intent") or "").strip().lower()
         if message_intent not in cls._RAG_QUERY_SCENES:
             return False
         if isinstance(rag_request_context, dict):
             action_mode = str(rag_request_context.get("action_mode") or "auto").strip().lower() or "auto"
             return action_mode not in {"write", "read_write"}
-        if str(source_system or "").strip().lower() == "feishu":
+        return str(source_system or "").strip().lower() == "feishu"
-            primary_workflow = (
-                str(semantic_bundle.get("primary_workflow") or "").strip().lower()
-                if isinstance(semantic_bundle, dict)
-                else ""
-            )
-            return primary_workflow == "query_flow" or message_intent in cls._RAG_QUERY_SCENES
-        return False
 
     def _coerce_intent_for_query_scene(
         self,
@@ -140,39 +172,27 @@ class KernelIngressFlowEntryMixin:
         semantic_bundle: Optional[dict[str, Any]],
         world_model: dict[str, Any],
     ) -> CanonicalIntent:
-        effective_semantic_bundle = (
-            semantic_bundle
-            if isinstance(semantic_bundle, dict) and semantic_bundle
-            else (intent.metadata or {}).get("semantic_bundle")
-            if isinstance((intent.metadata or {}).get("semantic_bundle"), dict)
-            else None
-        )
         scene_metadata = self._build_forced_query_scene_metadata(
             source_system=source_system,
             message_text=message_text,
             rag_request_context=rag_request_context,
-            semantic_bundle=effective_semantic_bundle,
+            semantic_bundle=semantic_bundle,
         )
+        intent_scene_metadata = self._extract_route_scene_metadata(intent.metadata)
+        if intent_scene_metadata:
+            scene_metadata = {
+                **scene_metadata,
+                **intent_scene_metadata,
+            }
         if not self._should_force_query_scene(
             source_system=source_system,
             rag_request_context=rag_request_context,
-            semantic_bundle=effective_semantic_bundle,
+            semantic_bundle=semantic_bundle,
             scene_metadata=scene_metadata,
         ):
             return intent
 
         project_id = _safe_int(intent.project_id) or _safe_int(world_model.get("resolved_project_id"))
-        semantic_bundle = WorkflowSemanticPlanner.plan_from_intent(
-            intent_name=INTENT_QUERY,
-            message_text=message_text,
-            source_system="ai_pmo_ingress",
-            workflow_route=ROUTE_QUERY_ANSWER,
-            project_id=project_id,
-            metadata={
-                "confidence": max(float(intent.confidence or 0.0), 0.86),
-                **scene_metadata,
-            },
-        ).to_dict()
         metadata = {
             **(intent.metadata or {}),
             **scene_metadata,
@@ -181,7 +201,6 @@ class KernelIngressFlowEntryMixin:
             "workflow_family": ROUTE_FAMILY_QUERY,
             "workflow_action": ROUTE_ACTION_ANSWER,
             "requires_confirmation": False,
-            "semantic_bundle": semantic_bundle,
         }
         pretty = re.sub(r"\s+", " ", str(message_text or "")).strip()
         return CanonicalIntent(
@@ -466,6 +485,91 @@ class KernelIngressFlowEntryMixin:
                     error=str(runtime_exc),
                 )
 
+        async def _safe_record_semantic_shadow(
+            *,
+            latest_user_message: str,
+            workflow_route: str,
+            route_metadata: Optional[dict[str, Any]],
+            operator_user_id: Optional[int],
+        ) -> None:
+            if mounted_tracking is None:
+                return
+            packet_summary: dict[str, Any] | None = None
+            try:
+                packet = await SemanticContextAssembler(self.db).assemble(
+                    tenant_id=int(self.tenant_id),
+                    operator_user_id=int(operator_user_id or 0),
+                    topic_workspace_id=int(mounted_tracking.topic_workspace.id),
+                    latest_user_message=latest_user_message,
+                )
+                packet_summary = {
+                    "packet_version": packet.packet_version,
+                    "topic_key": packet.topic.topic_key,
+                    "workflow_mode": packet.mode_card.workflow_mode,
+                    "recent_turn_count": len(packet.thread.recent_turns),
+                    "draft_version": packet.draft.draft_version if packet.draft is not None else None,
+                    "thread_summary": packet.thread.thread_summary,
+                    "recent_turns": [turn.model_dump(mode="json") for turn in packet.thread.recent_turns],
+                    "open_questions": list(packet.thread.open_questions),
+                    "mode_card": packet.mode_card.model_dump(mode="json"),
+                    "topic": packet.topic.model_dump(mode="json"),
+                    "draft": packet.draft.model_dump(mode="json") if packet.draft is not None else None,
+                }
+                decision = await SemanticDecisionService(llm_service).decide(packet=packet)
+                gate_result = SemanticDecisionGatekeeper.evaluate(
+                    mode_card=packet.mode_card,
+                    decision=decision,
+                )
+                await mounted_runtime.record_semantic_shadow(
+                    tracking=mounted_tracking,
+                    source_message_preview=latest_user_message,
+                    semantic_packet_summary=packet_summary,
+                    semantic_decision=decision.model_dump(mode="json"),
+                    semantic_gate_result=gate_result.model_dump(mode="json"),
+                    semantic_diff={
+                        "changed": (
+                            str(decision.mode_decision or "").strip().lower()
+                            != str(mounted_tracking.topic_workspace.plan_status or "").strip().lower()
+                        ),
+                        "legacy_route": str(workflow_route or "").strip() or None,
+                        "legacy_message_intent": str((route_metadata or {}).get("message_intent") or "").strip() or None,
+                        "shadow_topic_relation": decision.topic_relation,
+                        "shadow_mode_decision": decision.mode_decision,
+                    },
+                )
+            except Exception as runtime_exc:
+                logger.warning(
+                    "mounted_ai_runtime_semantic_shadow_failed",
+                    tenant_id=self.tenant_id,
+                    source_system=source_system,
+                    source_message_id=str(getattr(getattr(mounted_tracking, "conversation_run", None), "source_message_id", "") or ""),
+                    error=str(runtime_exc),
+                )
+                try:
+                    await mounted_runtime.record_semantic_shadow(
+                        tracking=mounted_tracking,
+                        source_message_preview=latest_user_message,
+                        semantic_packet_summary=packet_summary,
+                        semantic_decision=None,
+                        semantic_gate_result=None,
+                        semantic_diff={
+                            "changed": False,
+                            "legacy_route": str(workflow_route or "").strip() or None,
+                            "legacy_message_intent": str((route_metadata or {}).get("message_intent") or "").strip() or None,
+                            "shadow_error": str(runtime_exc),
+                        },
+                        status="failed",
+                        failure_reason=str(runtime_exc),
+                    )
+                except Exception as persist_exc:
+                    logger.warning(
+                        "mounted_ai_runtime_semantic_shadow_persist_failed",
+                        tenant_id=self.tenant_id,
+                        source_system=source_system,
+                        source_message_id=str(getattr(getattr(mounted_tracking, "conversation_run", None), "source_message_id", "") or ""),
+                        error=str(persist_exc),
+                    )
+
         def _resolve_specialist_agent_name(
             *,
             workflow_route: str,
@@ -541,8 +645,12 @@ class KernelIngressFlowEntryMixin:
             ingest_event=ingest_event,
             ingress_options=ingress_options,
         )
-        if self._should_route_to_draft_confirmation(
+        if await self._should_route_to_draft_confirmation_session(
             session=session,
+            sender=sender,
+            source_system=source_system,
+            channel_ref=channel_ref,
+            source_sender_open_id=source_sender_open_id,
             message_text=clean_text,
             ingress_options=ingress_options,
         ):
@@ -603,6 +711,26 @@ class KernelIngressFlowEntryMixin:
                 )
                 await _safe_finalize_mounted_runtime(result=draft_capture)
                 return draft_capture
+        await self._maybe_reset_session_project_context_for_new_requirement(
+            session=session,
+            sender=sender,
+            channel_ref=channel_ref,
+            source_sender_open_id=source_sender_open_id,
+            source_message_id=synthetic_message_id,
+            thread_context=thread_context,
+            message_text=clean_text,
+        )
+        orphan_requirement_followup_result = await self._maybe_handle_orphan_requirement_followup(
+            source_system=source_system,
+            source_message_id=synthetic_message_id,
+            channel_ref=channel_ref,
+            source_sender_open_id=source_sender_open_id,
+            ingress_options=ingress_options,
+            message_text=clean_text,
+        )
+        if orphan_requirement_followup_result is not None:
+            await _safe_finalize_mounted_runtime(result=orphan_requirement_followup_result)
+            return orphan_requirement_followup_result
         mixed_meta: dict[str, Any] = {}
         if not self._is_short_confirmation_message(
             message_text=clean_text,
@@ -706,6 +834,12 @@ class KernelIngressFlowEntryMixin:
             workflow_route.metadata = route_metadata
             perf_intent_name = workflow_route.key
             _log_perf("_resolve_intent", stage_started_at)
+            await _safe_record_semantic_shadow(
+                latest_user_message=clean_text,
+                workflow_route=workflow_route.key,
+                route_metadata=route_metadata,
+                operator_user_id=_safe_int(getattr(operator, "id", None)),
+            )
             if intent_context_source:
                 world_model["context_source"] = intent_context_source
             work_item.context_snapshot = world_model
@@ -1213,6 +1347,134 @@ class KernelIngressFlowEntryMixin:
             and WorkflowSemanticPlanner.looks_like_natural_draft_revision(message_text)
         )
 
+    async def _should_route_to_draft_confirmation_session(
+        self,
+        *,
+        session: ConversationSession,
+        sender: str,
+        source_system: str,
+        channel_ref: Optional[str],
+        source_sender_open_id: Optional[str],
+        message_text: str,
+        ingress_options: Optional[dict[str, Any]],
+    ) -> bool:
+        if WorkflowSemanticPlanner.looks_like_explicit_new_requirement_topic(message_text):
+            return False
+        if self._should_route_to_draft_confirmation(
+            session=session,
+            message_text=message_text,
+            ingress_options=ingress_options,
+        ):
+            return True
+
+        from ..draft_confirm_handler import DraftConfirmHandler
+
+        action, _ = DraftConfirmHandler.parse_action_from_text(message_text=message_text)
+        if action == DraftConfirmHandler._ACTION_UNKNOWN and not WorkflowSemanticPlanner.looks_like_natural_draft_revision(message_text):
+            return False
+
+        handler = DraftConfirmHandler(self)
+        return await handler.has_active_draft_session(
+            sender=sender,
+            source_system=source_system,
+            channel_ref=channel_ref,
+            source_sender_open_id=source_sender_open_id,
+            thread_context=self._extract_thread_context_from_ingress_options(ingress_options),
+        )
+
+    @staticmethod
+    def _build_orphan_requirement_followup_reply() -> str:
+        return "当前没有进行中的 A/B/C 收敛轮次，也没有待确认草稿。你先发一句新需求，我按新的 topic 接着收。"
+
+    async def _maybe_reset_session_project_context_for_new_requirement(
+        self,
+        *,
+        session: Optional[ConversationSession],
+        sender: str,
+        channel_ref: Optional[str],
+        source_sender_open_id: Optional[str],
+        source_message_id: Optional[str],
+        thread_context: Optional[dict[str, Any]],
+        message_text: str,
+    ) -> bool:
+        if session is None:
+            return False
+        if not WorkflowSemanticPlanner.looks_like_explicit_new_requirement_topic(message_text):
+            return False
+        if _safe_int(getattr(session, "current_project_id", None)) is None:
+            return False
+        await self._update_session(
+            tenant_id=self.tenant_id,
+            sender=sender,
+            session=session,
+            source_chat_id=channel_ref,
+            source_sender_open_id=source_sender_open_id,
+            source_message_id=source_message_id,
+            thread_context=thread_context,
+            current_project_id=None,
+            clear_current_project_id=True,
+            last_intent=str(getattr(session, "last_intent", "") or "").strip() or None,
+            last_message_text=str(getattr(session, "last_message_text", "") or "").strip() or None,
+        )
+        return True
+
+    async def _maybe_handle_orphan_requirement_followup(
+        self,
+        *,
+        source_system: str,
+        source_message_id: str,
+        channel_ref: Optional[str],
+        source_sender_open_id: Optional[str],
+        ingress_options: Optional[dict[str, Any]],
+        message_text: str,
+    ) -> Optional[dict[str, Any]]:
+        if not WorkflowSemanticPlanner.looks_like_low_context_requirement_followup(message_text):
+            return None
+        checker = getattr(self, "_has_active_requirement_plan_workspace", None)
+        if not callable(checker):
+            return None
+        thread_context = self._extract_thread_context_from_ingress_options(ingress_options)
+        is_feishu_source = str(source_system or "").strip().lower() == "feishu"
+        conversation_key = (
+            build_feishu_conversation_scope_key(
+                source_chat_id=channel_ref,
+                source_sender_open_id=source_sender_open_id,
+                sender=None,
+            )
+            if is_feishu_source
+            else source_message_id
+        )
+        has_active_requirement_plan_workspace = await checker(
+            source_system=source_system,
+            source_message_id=source_message_id,
+            source_chat_id=channel_ref,
+            sender=None,
+            source_sender_open_id=source_sender_open_id,
+            thread_context=thread_context,
+            conversation_key=conversation_key,
+            force_new_topic=False,
+        )
+        if has_active_requirement_plan_workspace:
+            return None
+        return {
+            "handled": True,
+            "status": "answered",
+            "classification": "question",
+            "linked_project_ids": [],
+            "signal_id": None,
+            "created_project_id": None,
+            "auto_tasks": [],
+            "ai_trace_id": None,
+            "results": [],
+            "intent": INTENT_QUERY,
+            "direct_reply_required": True,
+            "direct_reply_text": self._build_orphan_requirement_followup_reply(),
+            "reply_template_key": "requirement_plan_anchor_required",
+            "work_item_id": None,
+            "decision_level": None,
+            "review_summary": None,
+        }
+
     @classmethod
     def _merge_ingress_thread_context(
         cls,

backend/app/services/ai_pmo/draft_confirm_handler.py

@@ -12,8 +12,10 @@ for _name, _value in vars(_common).items():
         globals()[_name] = _value
 
 from ..ai_steward_service import AIStewardService
+from ..mounted_ai_runtime_write_service import MountedAIRuntimeWriteService
 from .workflow_semantic_planner import WorkflowSemanticPlanner
 from ..signal.draft_generator_service import DraftGeneratorService
+from ...models import MountedDraftWorkspace, MountedTopicWorkspace
 from ...models.feishu_reply_outbox import FeishuReplyOutbox
 
 
@@ -52,6 +54,7 @@ class DraftConfirmHandler:
     ) -> dict[str, Any]:
         action, revision_note = self.parse_action_from_text(message_text=message_text)
         explicit_work_item_id = self._extract_work_item_id(message_text)
+        looks_like_revision = WorkflowSemanticPlanner.looks_like_natural_draft_revision(message_text)
 
         target = await self._resolve_pending_draft(
             sender=sender,
@@ -62,11 +65,11 @@ class DraftConfirmHandler:
             thread_context=thread_context,
         )
         if target is None:
-            if explicit_work_item_id is not None:
+            if explicit_work_item_id is not None or action != self._ACTION_UNKNOWN or looks_like_revision:
                 return {
-                    "handled": False,
+                    "handled": True,
-                    "status": "ignored",
+                    "status": "failed_safe",
-                    "followup_reply": "",
+                    "followup_reply": "当前没有可确认草稿。请先在当前会话重新生成最新草稿，再回复“确认”或“修改”。",
                     "target_work_item_id": None,
                     "target_state": None,
                     "target_intent": None,
@@ -646,6 +649,25 @@ class DraftConfirmHandler:
         explicit_work_item_id: Optional[int],
         thread_context: Optional[dict[str, Any]],
     ) -> Optional[tuple[WorkItem, IngestEvent]]:
+        if explicit_work_item_id is not None:
+            return await self._load_work_item_target_by_id(work_item_id=int(explicit_work_item_id))
+
+        active_mounted = await self._resolve_active_mounted_draft(
+            sender=sender,
+            source_system=source_system,
+            channel_ref=channel_ref,
+            source_sender_open_id=source_sender_open_id,
+            thread_context=thread_context,
+        )
+        if active_mounted is not None:
+            mounted_draft, _ = active_mounted
+            active_work_item_id = _safe_int(getattr(mounted_draft, "active_work_item_id", None))
+            if active_work_item_id is not None:
+                target = await self._load_work_item_target_by_id(work_item_id=int(active_work_item_id))
+                if target is not None:
+                    return target
+            return None
+
         stmt = (
             select(WorkItem, IngestEvent)
             .join(IngestEvent, IngestEvent.id == WorkItem.ingest_event_id)
@@ -659,11 +681,6 @@ class DraftConfirmHandler:
             .limit(30)
         )
         rows = (await self.db.execute(stmt)).all()
-        if explicit_work_item_id is not None:
-            for work_item, ingest in rows:
-                if int(work_item.id) == int(explicit_work_item_id):
-                    return work_item, ingest
-            return None
 
         normalized_sender = str(sender or "").strip()
         normalized_channel = str(channel_ref or "").strip()
@@ -738,6 +755,143 @@ class DraftConfirmHandler:
             return None
         return candidates[0]
 
+    async def has_active_draft_session(
+        self,
+        *,
+        sender: str,
+        source_system: str,
+        channel_ref: Optional[str],
+        source_sender_open_id: Optional[str],
+        thread_context: Optional[dict[str, Any]],
+    ) -> bool:
+        if not hasattr(self.db, "execute"):
+            return False
+        mounted = await self._resolve_active_mounted_draft(
+            sender=sender,
+            source_system=source_system,
+            channel_ref=channel_ref,
+            source_sender_open_id=source_sender_open_id,
+            thread_context=thread_context,
+        )
+        return mounted is not None
+
+    async def _load_work_item_target_by_id(
+        self,
+        *,
+        work_item_id: int,
+    ) -> Optional[tuple[WorkItem, IngestEvent]]:
+        row = (
+            await self.db.execute(
+                select(WorkItem, IngestEvent)
+                .join(IngestEvent, IngestEvent.id == WorkItem.ingest_event_id)
+                .where(
+                    and_(
+                        WorkItem.tenant_id == int(self.tenant_id),
+                        WorkItem.id == int(work_item_id),
+                    )
+                )
+                .limit(1)
+            )
+        ).first()
+        if row is None:
+            return None
+        return row
+
+    async def _resolve_active_mounted_draft(
+        self,
+        *,
+        sender: str,
+        source_system: str,
+        channel_ref: Optional[str],
+        source_sender_open_id: Optional[str],
+        thread_context: Optional[dict[str, Any]],
+    ) -> Optional[tuple[MountedDraftWorkspace, MountedTopicWorkspace]]:
+        normalized_source = str(source_system or "").strip().lower()
+        if not normalized_source:
+            return None
+        conversation_scope_key = MountedAIRuntimeWriteService.build_conversation_scope_key(
+            source_system=normalized_source,
+            channel_ref=channel_ref,
+            sender=sender,
+            source_sender_open_id=source_sender_open_id,
+        )
+        anchor_context = self._normalize_thread_context(thread_context)
+
+        rows = (
+            await self.db.execute(
+                select(MountedDraftWorkspace, MountedTopicWorkspace)
+                .join(
+                    MountedTopicWorkspace,
+                    MountedTopicWorkspace.id == MountedDraftWorkspace.topic_workspace_id,
+                )
+                .where(
+                    and_(
+                        MountedDraftWorkspace.tenant_id == int(self.tenant_id),
+                        MountedTopicWorkspace.tenant_id == int(self.tenant_id),
+                        MountedTopicWorkspace.source_system == normalized_source,
+                        MountedTopicWorkspace.mode == "plan",
+                        MountedTopicWorkspace.status == "active",
+                        MountedDraftWorkspace.state.in_(("ready", STATE_DRAFT_PENDING_CONFIRM)),
+                    )
+                )
+                .order_by(MountedTopicWorkspace.updated_at.desc(), MountedDraftWorkspace.id.desc())
+            )
+        ).all()
+        if not rows:
+            return None
+
+        normalized_channel = str(channel_ref or "").strip()
+        best: Optional[tuple[MountedDraftWorkspace, MountedTopicWorkspace]] = None
+        best_score = -1
+        for draft_row, topic_row in rows:
+            if not hasattr(draft_row, "topic_workspace_id") or not hasattr(topic_row, "mode"):
+                continue
+            score = 0
+            if normalized_channel and str(getattr(topic_row, "channel_ref", "") or "").strip() == normalized_channel:
+                score += 30
+            if conversation_scope_key and str(getattr(topic_row, "conversation_scope_key", "") or "").strip() == str(conversation_scope_key).strip():
+                score += 60
+            if anchor_context and self._matches_mounted_draft_anchor(
+                draft=draft_row,
+                topic=topic_row,
+                anchor_context=anchor_context,
+            ):
+                score += 120
+            if str(getattr(topic_row, "plan_status", "") or "").strip().lower() in {
+                STATE_DRAFT_PENDING_CONFIRM,
+                "awaiting_execute_confirm",
+                "draft_ready",
+            }:
+                score += 20
+            if _safe_int(getattr(draft_row, "active_work_item_id", None)) is not None:
+                score += 10
+            if score > best_score:
+                best = (draft_row, topic_row)
+                best_score = score
+        if best_score <= 0:
+            return None
+        return best
+
+    @classmethod
+    def _matches_mounted_draft_anchor(
+        cls,
+        *,
+        draft: MountedDraftWorkspace,
+        topic: MountedTopicWorkspace,
+        anchor_context: dict[str, str],
+    ) -> bool:
+        if not anchor_context:
+            return False
+        candidate_values = {
+            str(getattr(draft, "latest_reply_message_id", "") or "").strip(),
+            str(getattr(draft, "latest_source_message_id", "") or "").strip(),
+            str(getattr(topic, "root_message_id", "") or "").strip(),
+            str(getattr(topic, "thread_key", "") or "").strip(),
+            str(getattr(topic, "last_message_id", "") or "").strip(),
+        }
+        candidate_values.discard("")
+        return any(str(value or "").strip() in candidate_values for value in anchor_context.values())
+
     @staticmethod
     def _normalize_thread_context(
         thread_context: Optional[dict[str, Any]],

backend/app/services/ai_pmo/semantic_context/__init__.py

@@ -0,0 +1,25 @@
+from .assembler import SemanticContextAssembler
+from .decision_schema import DecisionGateResult, ModeCard, SemanticDecision
+from .gatekeeper import SemanticDecisionGatekeeper
+from .mode_card_service import build_mode_card
+from .packet_schema import (
+    SemanticContextDraftSnapshot,
+    SemanticContextPacket,
+    SemanticContextThread,
+    SemanticContextTopic,
+    SemanticContextTurn,
+)
+
+__all__ = [
+    "DecisionGateResult",
+    "ModeCard",
+    "SemanticContextAssembler",
+    "SemanticContextDraftSnapshot",
+    "SemanticContextPacket",
+    "SemanticContextThread",
+    "SemanticContextTopic",
+    "SemanticContextTurn",
+    "SemanticDecision",
+    "SemanticDecisionGatekeeper",
+    "build_mode_card",
+]

backend/app/services/ai_pmo/semantic_context/assembler.py

@@ -0,0 +1,178 @@
+from __future__ import annotations
+
+from typing import Any, Optional
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ....models import MountedConversationRun, MountedDraftWorkspace, MountedTopicWorkspace
+from .mode_card_service import build_mode_card
+from .packet_schema import (
+    SemanticContextDraftSnapshot,
+    SemanticContextPacket,
+    SemanticContextThread,
+    SemanticContextTopic,
+    SemanticContextTurn,
+)
+
+
+class SemanticContextAssembler:
+    def __init__(self, db: AsyncSession, *, recent_turn_limit: int = 6):
+        self.db = db
+        self.recent_turn_limit = max(int(recent_turn_limit), 2)
+
+    @staticmethod
+    def _normalize_list(value: Any) -> list[str]:
+        if isinstance(value, list):
+            return [str(item).strip() for item in value if str(item or "").strip()]
+        return []
+
+    @staticmethod
+    def _normalize_mapping(value: Any) -> Optional[Any]:
+        if isinstance(value, (dict, list)):
+            return value
+        return None
+
+    @staticmethod
+    def _extract_source_preview(row: MountedConversationRun) -> Optional[str]:
+        execution_outcome = row.execution_outcome if isinstance(row.execution_outcome, dict) else {}
+        preview = str(execution_outcome.get("source_message_preview") or "").strip()
+        return preview or None
+
+    async def _load_topic(
+        self,
+        *,
+        tenant_id: int,
+        topic_workspace_id: int,
+    ) -> MountedTopicWorkspace:
+        topic_row = (
+            await self.db.execute(
+                select(MountedTopicWorkspace).where(
+                    MountedTopicWorkspace.tenant_id == int(tenant_id),
+                    MountedTopicWorkspace.id == int(topic_workspace_id),
+                )
+            )
+        ).scalar_one_or_none()
+        if topic_row is None:
+            raise ValueError("mounted topic workspace not found")
+        return topic_row
+
+    async def _load_draft(
+        self,
+        *,
+        tenant_id: int,
+        topic_workspace_id: int,
+    ) -> MountedDraftWorkspace | None:
+        return (
+            await self.db.execute(
+                select(MountedDraftWorkspace).where(
+                    MountedDraftWorkspace.tenant_id == int(tenant_id),
+                    MountedDraftWorkspace.topic_workspace_id == int(topic_workspace_id),
+                )
+            )
+        ).scalar_one_or_none()
+
+    async def _load_recent_turns(
+        self,
+        *,
+        tenant_id: int,
+        topic_workspace_id: int,
+    ) -> list[SemanticContextTurn]:
+        run_rows = (
+            await self.db.execute(
+                select(MountedConversationRun)
+                .where(
+                    MountedConversationRun.tenant_id == int(tenant_id),
+                    MountedConversationRun.topic_workspace_id == int(topic_workspace_id),
+                    MountedConversationRun.status == "completed",
+                )
+                .order_by(MountedConversationRun.updated_at.desc(), MountedConversationRun.id.desc())
+                .limit(max(self.recent_turn_limit, 1))
+            )
+        ).scalars().all()
+        turns: list[SemanticContextTurn] = []
+        for row in reversed(run_rows):
+            source_preview = self._extract_source_preview(row)
+            if source_preview:
+                turns.append(SemanticContextTurn(role="user", text=source_preview))
+            final_reply = str(row.final_reply or "").strip()
+            if final_reply:
+                turns.append(SemanticContextTurn(role="assistant", text=final_reply))
+        if len(turns) <= self.recent_turn_limit:
+            return turns
+        return turns[-self.recent_turn_limit :]
+
+    async def assemble(
+        self,
+        *,
+        tenant_id: int,
+        operator_user_id: int,
+        topic_workspace_id: int,
+        latest_user_message: str,
+    ) -> SemanticContextPacket:
+        topic_row = await self._load_topic(
+            tenant_id=tenant_id,
+            topic_workspace_id=topic_workspace_id,
+        )
+        draft_row = await self._load_draft(
+            tenant_id=tenant_id,
+            topic_workspace_id=topic_workspace_id,
+        )
+        recent_turns = await self._load_recent_turns(
+            tenant_id=tenant_id,
+            topic_workspace_id=topic_workspace_id,
+        )
+
+        topic = SemanticContextTopic(
+            topic_workspace_id=int(topic_row.id),
+            topic_key=topic_row.topic_key,
+            source_system=topic_row.source_system,
+            channel_ref=topic_row.channel_ref,
+            conversation_scope_key=topic_row.conversation_scope_key,
+            thread_key=topic_row.thread_key,
+            mode=topic_row.mode,
+            status=topic_row.status,
+            plan_status=topic_row.plan_status,
+            facts=self._normalize_mapping(topic_row.facts_json),
+            goals=self._normalize_mapping(topic_row.goals_json),
+            constraints=self._normalize_mapping(topic_row.constraints_json),
+            metadata=self._normalize_mapping(topic_row.metadata_json),
+        )
+        draft = None
+        if draft_row is not None:
+            draft = SemanticContextDraftSnapshot(
+                draft_workspace_id=int(draft_row.id),
+                state=draft_row.state,
+                draft_version=int(draft_row.draft_version or 0),
+                requirement_card=self._normalize_mapping(draft_row.requirement_card),
+                draft_payload=self._normalize_mapping(draft_row.draft_payload),
+                required_confirmations=self._normalize_list(draft_row.required_confirmations),
+            )
+
+        metadata = topic_row.metadata_json if isinstance(topic_row.metadata_json, dict) else {}
+        thread = SemanticContextThread(
+            thread_key=topic_row.thread_key,
+            conversation_scope_key=topic_row.conversation_scope_key,
+            thread_summary=str(
+                metadata.get("last_message_preview")
+                or (topic_row.facts_json or {}).get("title")
+                or ""
+            ).strip()
+            or None,
+            recent_turns=recent_turns,
+            open_questions=self._normalize_list(topic_row.open_questions),
+        )
+        return SemanticContextPacket(
+            packet_version="v1",
+            tenant_id=int(tenant_id),
+            operator_user_id=int(operator_user_id),
+            topic_workspace_id=int(topic_row.id),
+            latest_user_message=str(latest_user_message or "").strip(),
+            topic=topic,
+            draft=draft,
+            thread=thread,
+            mode_card=build_mode_card(
+                topic_mode=topic_row.mode,
+                plan_status=topic_row.plan_status,
+            ),
+        )

backend/app/services/ai_pmo/semantic_context/decision_schema.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class ModeCard(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    workflow_mode: Literal["CHAT", "PLAN", "EXECUTE"] = "CHAT"
+    mode_state: str = "IDLE"
+    allowed_actions: list[str] = Field(default_factory=list)
+    forbidden_actions: list[str] = Field(default_factory=list)
+    exit_conditions: list[str] = Field(default_factory=list)
+
+
+class SemanticDecision(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    topic_relation: Literal["continue_current_topic", "new_topic", "uncertain"]
+    mode_decision: Literal["stay_plan", "ready_confirm", "request_execute", "answer_only"]
+    draft_patch: dict[str, Any] = Field(default_factory=dict)
+    assistant_reply: str = ""
+    requires_confirmation: bool = False
+    requested_actions: list[str] = Field(default_factory=list)
+    confidence: float = 0.0
+    uncertainties: list[str] = Field(default_factory=list)
+
+
+class DecisionGateResult(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    accepted: bool
+    blocked_reasons: list[str] = Field(default_factory=list)
+    allowed_actions: list[str] = Field(default_factory=list)
+    next_plan_status: str
+    needs_confirmation: bool = False
+    mode_state_after: str | None = None

backend/app/services/ai_pmo/semantic_context/gatekeeper.py

@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from .decision_schema import DecisionGateResult, ModeCard, SemanticDecision
+
+
+class SemanticDecisionGatekeeper:
+    @staticmethod
+    def _next_plan_status(*, accepted: bool, decision: SemanticDecision) -> str:
+        if not accepted:
+            return "active"
+        if decision.mode_decision == "ready_confirm":
+            return "awaiting_execute_confirm"
+        if decision.mode_decision == "request_execute":
+            return "execution_pending"
+        if decision.mode_decision == "answer_only":
+            return "idle"
+        return "active"
+
+    @classmethod
+    def evaluate(
+        cls,
+        *,
+        mode_card: ModeCard,
+        decision: SemanticDecision,
+    ) -> DecisionGateResult:
+        blocked_reasons: list[str] = []
+        allowed_actions: list[str] = []
+        allowed_set = {str(item).strip() for item in mode_card.allowed_actions if str(item or "").strip()}
+        forbidden_set = {str(item).strip() for item in mode_card.forbidden_actions if str(item or "").strip()}
+
+        for action in decision.requested_actions:
+            normalized_action = str(action or "").strip()
+            if not normalized_action:
+                continue
+            if normalized_action in forbidden_set:
+                blocked_reasons.append(f"forbidden_action:{normalized_action}")
+                continue
+            if allowed_set and normalized_action not in allowed_set:
+                blocked_reasons.append(f"unapproved_action:{normalized_action}")
+                continue
+            allowed_actions.append(normalized_action)
+
+        accepted = len(blocked_reasons) == 0
+        return DecisionGateResult(
+            accepted=accepted,
+            blocked_reasons=blocked_reasons,
+            allowed_actions=allowed_actions,
+            next_plan_status=cls._next_plan_status(accepted=accepted, decision=decision),
+            needs_confirmation=bool(decision.requires_confirmation),
+            mode_state_after=decision.mode_decision,
+        )

backend/app/services/ai_pmo/semantic_context/mode_card_service.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+from .decision_schema import ModeCard
+
+
+def build_mode_card(*, topic_mode: str | None, plan_status: str | None) -> ModeCard:
+    normalized_mode = str(topic_mode or "chat").strip().lower()
+    normalized_plan_status = str(plan_status or "idle").strip().lower()
+    workflow_mode = {
+        "plan": "PLAN",
+        "execute": "EXECUTE",
+    }.get(normalized_mode, "CHAT")
+
+    if workflow_mode == "PLAN":
+        return ModeCard(
+            workflow_mode="PLAN",
+            mode_state=normalized_plan_status.upper() or "ACTIVE",
+            allowed_actions=[
+                "reply.compose",
+                "draft.patch",
+                "clarify.ask",
+                "topic.continue_or_split",
+                "plan.mark_ready_for_confirmation",
+            ],
+            forbidden_actions=[
+                "project.create",
+                "task.create",
+                "signal_source.onboard",
+                "db.write",
+            ],
+            exit_conditions=[
+                "用户明确要求开始执行",
+                "草稿完成且需要确认项已补齐",
+            ],
+        )
+
+    if workflow_mode == "EXECUTE":
+        return ModeCard(
+            workflow_mode="EXECUTE",
+            mode_state=normalized_plan_status.upper() or "ACTIVE",
+            allowed_actions=[
+                "reply.compose",
+                "project.create",
+                "task.create",
+                "followup.execute",
+            ],
+            forbidden_actions=["db.write"],
+            exit_conditions=["执行完成或执行失败后返回结果"],
+        )
+
+    return ModeCard(
+        workflow_mode="CHAT",
+        mode_state=normalized_plan_status.upper() or "IDLE",
+        allowed_actions=["reply.compose", "clarify.ask"],
+        forbidden_actions=["project.create", "task.create", "db.write"],
+        exit_conditions=["用户提出新的规划或执行需求"],
+    )

backend/app/services/ai_pmo/semantic_context/packet_schema.py

@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from typing import Any, Optional
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from .decision_schema import ModeCard
+
+
+class SemanticContextTurn(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    role: str
+    text: str
+
+
+class SemanticContextThread(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    thread_key: Optional[str] = None
+    conversation_scope_key: Optional[str] = None
+    thread_summary: Optional[str] = None
+    recent_turns: list[SemanticContextTurn] = Field(default_factory=list)
+    open_questions: list[str] = Field(default_factory=list)
+
+
+class SemanticContextTopic(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    topic_workspace_id: int
+    topic_key: str
+    source_system: str
+    channel_ref: Optional[str] = None
+    conversation_scope_key: Optional[str] = None
+    thread_key: Optional[str] = None
+    mode: str
+    status: str
+    plan_status: Optional[str] = None
+    facts: Optional[Any] = None
+    goals: Optional[Any] = None
+    constraints: Optional[Any] = None
+    metadata: Optional[Any] = None
+
+
+class SemanticContextDraftSnapshot(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    draft_workspace_id: int
+    state: str
+    draft_version: int
+    requirement_card: Optional[Any] = None
+    draft_payload: Optional[Any] = None
+    required_confirmations: list[str] = Field(default_factory=list)
+
+
+class SemanticContextPacket(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    packet_version: str = "v1"
+    tenant_id: int
+    operator_user_id: int
+    topic_workspace_id: int
+    latest_user_message: str
+    topic: SemanticContextTopic
+    draft: SemanticContextDraftSnapshot | None = None
+    thread: SemanticContextThread
+    mode_card: ModeCard

backend/app/services/ai_pmo/semantic_context/semantic_decision_service.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from ....config.ai_prompts import PMFLOW_SEMANTIC_CONTEXT_DECISION_PROMPT
+from ....services.llm_service.service import LLMService
+from .decision_schema import SemanticDecision
+from .packet_schema import SemanticContextPacket
+
+
+class SemanticDecisionService:
+    FUNCTION_NAME = "pmflow_semantic_context_decision"
+
+    def __init__(self, llm_service: LLMService):
+        self.llm_service = llm_service
+
+    @staticmethod
+    def build_prompt(packet: SemanticContextPacket) -> str:
+        return PMFLOW_SEMANTIC_CONTEXT_DECISION_PROMPT.format(
+            semantic_packet=json.dumps(packet.model_dump(mode="json"), ensure_ascii=False, indent=2)
+        )
+
+    async def decide(self, *, packet: SemanticContextPacket) -> SemanticDecision:
+        structured = await self.llm_service.generate_structured_object(
+            prompt=self.build_prompt(packet),
+            function_name=self.FUNCTION_NAME,
+            use_prompt_profile=False,
+            temperature=0.0,
+            response_mode="fast",
+        )
+        payload: Any = getattr(structured, "payload", None)
+        if payload is None and hasattr(structured, "model_dump"):
+            payload = structured.model_dump()
+        if payload is None:
+            payload = structured
+        if not isinstance(payload, dict):
+            raise ValueError("semantic decision payload is not a dict")
+        return SemanticDecision.model_validate(payload)

backend/app/services/ai_pmo/workflow_semantic_planner/service.py

@@ -263,6 +263,28 @@ class WorkflowSemanticPlanner:
             return True
         return False
 
+    @classmethod
+    def looks_like_requirement_plan_choice_reply(cls, text: str) -> bool:
+        return bool(re.match(r"^\s*([ABCabc])(?:[\.\,，。、:：)\]】\s]|$)", str(text or "")))
+
+    @classmethod
+    def looks_like_low_context_requirement_followup(cls, text: str) -> bool:
+        raw = str(text or "").strip()
+        if not raw:
+            return False
+        if cls._looks_like_casual_chat_message(raw):
+            return False
+        if cls.looks_like_explicit_new_requirement_topic(raw):
+            return False
+        if cls._has_question_structure(raw) or cls.is_obvious_question(raw):
+            return False
+        if cls.looks_like_requirement_plan_choice_reply(raw):
+            return True
+        normalized = cls._normalize_text(raw)
+        if normalized in DRAFT_CONFIRM_EXACT_HINTS:
+            return True
+        return normalized in {"你自行推荐", "你来推荐", "你来定", "你决定"}
+
     @classmethod
     def _looks_like_requirement_plan_slot_answer(cls, text: str) -> bool:
         normalized = cls._normalize_text(text)

backend/app/services/context_cache_service/mixin_cache_core.py

@@ -75,10 +75,27 @@ class ContextCacheCoreMixin:
             pass
 
     @staticmethod
-    def _is_system_snapshot_complete(payload: Optional[dict[str, Any]]) -> bool:
+    def _expected_system_snapshot_source_scan() -> dict[str, str]:
+        backend_root = Path(__file__).resolve().parents[3]
+        workspace_root = backend_root.parent
+        app_root = backend_root / "app"
+        return {
+            "backend_root": str(backend_root),
+            "app_root": str(app_root),
+            "workspace_root": str(workspace_root),
+        }
+
+    def _is_system_snapshot_complete(self, payload: Optional[dict[str, Any]]) -> bool:
         if not isinstance(payload, dict):
             return False
-        return isinstance(payload.get("assistant_profile"), dict) and isinstance(payload.get("platform_profile"), dict)
+        if str(payload.get("schema_version") or "").strip() != SYSTEM_SNAPSHOT_SCHEMA_VERSION:
+            return False
+        if not isinstance(payload.get("assistant_profile"), dict) or not isinstance(payload.get("platform_profile"), dict):
+            return False
+        platform_profile = payload.get("platform_profile") if isinstance(payload.get("platform_profile"), dict) else {}
+        source_scan = platform_profile.get("source_scan") if isinstance(platform_profile.get("source_scan"), dict) else {}
+        expected = self._expected_system_snapshot_source_scan()
+        return all(str(source_scan.get(key) or "").strip() == value for key, value in expected.items())
 
     async def get_system_snapshot(self, *, db: AsyncSession, tenant_id: int) -> dict[str, Any]:
         tid = _safe_int(tenant_id, 1)
@@ -123,6 +140,7 @@ class ContextCacheCoreMixin:
         platform_profile = self._build_platform_profile()
         return {
             "tenant_id": tenant_id,
+            "schema_version": SYSTEM_SNAPSHOT_SCHEMA_VERSION,
             "assistant_profile": assistant_profile,
             "platform_profile": platform_profile,
             "projects": projects.get("items", []),
@@ -130,4 +148,3 @@ class ContextCacheCoreMixin:
             "pending_signals": pending,
             "snapshot_at": now_cn_aware().isoformat(),
         }
-

backend/app/services/context_cache_service/mixin_profile_route_parser.py

@@ -190,7 +190,7 @@ class ContextCacheProfileRouteParserMixin:
         }
 
     def _build_platform_profile(self) -> dict[str, Any]:
-        backend_root = Path(__file__).resolve().parents[2]
+        backend_root = Path(__file__).resolve().parents[3]
         workspace_root = backend_root.parent
         app_root = backend_root / "app"
         api_dir = app_root / "api"
@@ -246,6 +246,8 @@ class ContextCacheProfileRouteParserMixin:
             "feature_catalog": feature_catalog.get("items") if isinstance(feature_catalog.get("items"), list) else [],
             "source_scan": {
                 "backend_root": str(backend_root),
+                "app_root": str(app_root),
+                "workspace_root": str(workspace_root),
                 "app_python_files": app_python_files,
                 "feature_catalog_source": str(feature_catalog.get("source") or "unknown"),
                 "scanned_at": now_cn_aware().isoformat(),

backend/app/services/context_cache_service/shared.py

@@ -27,6 +27,7 @@ except Exception:  # pragma: no cover
     RedisSync = None
 
 
+SYSTEM_SNAPSHOT_SCHEMA_VERSION = "2026-04-07.context-v2"
 SYSTEM_TTL_SECONDS = 30 * 60
 USER_TTL_SECONDS = 5 * 60
 SESSION_TTL_SECONDS = 60 * 60

backend/app/services/integrations/feishu_integration_service/mixin_reply_outbox.py

@@ -328,6 +328,22 @@ class FeishuReplyOutboxMixin:
                 row.last_error = None
                 if sent_message_id:
                     row.sent_message_id = sent_message_id[:128]
+                    if str(row.source_message_id or "").strip():
+                        from app.models import MountedDraftWorkspace
+
+                        mounted_rows = (
+                            await db_update.execute(
+                                select(MountedDraftWorkspace).where(
+                                    and_(
+                                        MountedDraftWorkspace.tenant_id == int(row.tenant_id),
+                                        MountedDraftWorkspace.latest_source_message_id == str(row.source_message_id).strip()[:128],
+                                        MountedDraftWorkspace.state.in_(("ready", "draft_pending_confirm")),
+                                    )
+                                )
+                            )
+                        ).scalars().all()
+                        for mounted_row in mounted_rows:
+                            mounted_row.latest_reply_message_id = sent_message_id[:128]
                 await db_update.commit()
                 perf_t2 = time.perf_counter()
                 perf_t0 = _OUTBOX_PERF_T0_BY_ID.pop(outbox_id, None)
@@ -357,11 +373,67 @@ class FeishuReplyOutboxMixin:
 
     @classmethod
     def _normalize_outgoing_reply_text(cls, value: Optional[str]) -> str:
-        text = cls._clean_reply_text(value)
+        cleaner = getattr(cls, "_clean_reply_text", None)
-        if (not text) or cls._looks_like_provider_error(text):
+        if callable(cleaner):
-            return cls._build_error_reply_text()
+            text = cleaner(value)
+        else:
+            text = str(value or "").strip()
+            if text.startswith("```"):
+                text = text.replace("```", "").strip()
+            if text.lower().startswith("json"):
+                text = text[4:].strip()
+        looks_like_provider_error = getattr(cls, "_looks_like_provider_error", None)
+        has_provider_error = callable(looks_like_provider_error) and bool(looks_like_provider_error(text))
+        if not text or has_provider_error:
+            error_builder = getattr(cls, "_build_error_reply_text", None)
+            if callable(error_builder):
+                return error_builder()
+            return "当前回复生成失败，请稍后重试。"
+        text = cls._beautify_outgoing_reply_text(text)
         return text[:800]
 
+    @staticmethod
+    def _beautify_outgoing_reply_text(value: Optional[str]) -> str:
+        raw = str(value or "").strip()
+        if not raw:
+            return ""
+
+        label_map = {
+            "结论：": "🎯 结论：",
+            "依据：": "🔎 依据：",
+            "下一步：": "➡️ 下一步：",
+            "信息不足，缺少：": "🧩 信息不足，缺少：",
+            "当前我更推荐：": "⭐ 当前我更推荐：",
+        }
+        section_prefixes = tuple(label_map.values()) + ("🧭 第 ",)
+        choice_prefixes = ("A.", "B.", "C.")
+        tail_prefixes = ("你回 A / B / C",)
+
+        beautified: list[str] = []
+        for raw_line in raw.splitlines():
+            stripped = str(raw_line).strip()
+            if not stripped:
+                if beautified and beautified[-1] != "":
+                    beautified.append("")
+                continue
+            for plain, pretty in label_map.items():
+                if stripped.startswith(plain):
+                    stripped = f"{pretty}{stripped[len(plain):]}"
+                    break
+            needs_leading_gap = (
+                any(stripped.startswith(prefix) for prefix in section_prefixes)
+                or stripped in choice_prefixes
+                or any(stripped.startswith(prefix) for prefix in choice_prefixes)
+                or any(stripped.startswith(prefix) for prefix in tail_prefixes)
+            )
+            if needs_leading_gap and beautified and beautified[-1] != "":
+                beautified.append("")
+            beautified.append(stripped)
+
+        text = "\n".join(beautified).strip()
+        text = re.sub(r"\n{3,}", "\n\n", text)
+        return text
+
     @staticmethod
     def _build_reply_hash(*, source_message_id: Optional[str], message_text: str) -> Optional[str]:
         source = str(source_message_id or "").strip()

backend/app/services/llm_service/mixin_engine_resolution_core.py

@@ -5,7 +5,6 @@ from .shared import *
 class LLMEngineResolutionCoreMixin:
     _PROTECTED_QUERY_HIGH_FUNCTIONS = frozenset({
         "adaptive_rag_answer_deep",
-        "adaptive_rag_answer_normal",
     })
 
     def _stub_response(self, prompt: str) -> str:

backend/app/services/llm_service/shared_part_01.py

@@ -265,6 +265,29 @@ STRUCTURED_OBJECT_SCHEMAS: dict[str, dict[str, Any]] = {
             "requires_confirmation": ("bool",),
         },
     },
+    "pmflow_semantic_context_decision": {
+        "required_keys": (
+            "topic_relation",
+            "mode_decision",
+            "draft_patch",
+            "assistant_reply",
+            "requires_confirmation",
+            "requested_actions",
+            "confidence",
+        ),
+        "type_checks": {
+            "topic_relation": "str",
+            "mode_decision": "str",
+            "draft_patch": "dict",
+            "assistant_reply": "str",
+            "requires_confirmation": "bool",
+            "requested_actions": "list",
+            "confidence": "number",
+        },
+        "optional_type_checks": {
+            "uncertainties": ("list",),
+        },
+    },
     "signal_planner_analyze": {
         "required_keys": ("summary", "priority", "execution_plan", "risks", "confidence"),
         "type_checks": {

backend/app/services/mounted_ai_runtime_service.py

@@ -129,6 +129,19 @@ class MountedAIRuntimeService:
             "updated_at": self._dt(row.updated_at),
         }
 
+    def _draft_snapshot(self, row: MountedDraftWorkspace) -> dict[str, Any]:
+        return {
+            "id": int(row.id),
+            "topic_workspace_id": int(row.topic_workspace_id),
+            "state": row.state,
+            "draft_version": int(row.draft_version or 0),
+            "requirement_card": row.requirement_card,
+            "draft_payload": row.draft_payload,
+            "required_confirmations": row.required_confirmations,
+            "last_ready_at": self._dt(row.last_ready_at),
+            "confirm_message_id": row.confirm_message_id,
+        }
+
     @staticmethod
     def _source_activation_snapshot(row: MountedSourceActivation) -> dict[str, Any]:
         return {
@@ -547,13 +560,56 @@ class MountedAIRuntimeService:
                 .order_by(MountedAgentTrace.id.asc())
             )
         ).scalars().all()
+        topic_row: MountedTopicWorkspace | None = None
+        draft_row: MountedDraftWorkspace | None = None
+        if row.topic_workspace_id is not None:
+            topic_row = (
+                await self.db.execute(
+                    select(MountedTopicWorkspace).where(
+                        MountedTopicWorkspace.tenant_id == int(tenant_id),
+                        MountedTopicWorkspace.id == int(row.topic_workspace_id),
+                    )
+                )
+            ).scalar_one_or_none()
+            if topic_row is not None:
+                draft_row = (
+                    await self.db.execute(
+                        select(MountedDraftWorkspace).where(
+                            MountedDraftWorkspace.tenant_id == int(tenant_id),
+                            MountedDraftWorkspace.topic_workspace_id == int(topic_row.id),
+                        )
+                    )
+                ).scalar_one_or_none()
+        execution_outcome = row.execution_outcome if isinstance(row.execution_outcome, dict) else row.execution_outcome
 
         return {
             "item": self._run_snapshot(row, trace_count=len(trace_rows)),
+            "topic": self._topic_snapshot(topic_row, draft=draft_row) if topic_row is not None else None,
+            "draft": self._draft_snapshot(draft_row) if draft_row is not None else None,
             "agent_traces": [self._trace_snapshot(trace) for trace in trace_rows],
             "context_layers_hit": row.context_layers_hit,
             "tool_calls": row.tool_calls,
-            "execution_outcome": row.execution_outcome,
+            "execution_outcome": execution_outcome,
+            "semantic_packet_summary": (
+                execution_outcome.get("semantic_packet_summary")
+                if isinstance(execution_outcome, dict)
+                else None
+            ),
+            "semantic_decision": (
+                execution_outcome.get("semantic_decision")
+                if isinstance(execution_outcome, dict)
+                else None
+            ),
+            "semantic_gate_result": (
+                execution_outcome.get("semantic_gate_result")
+                if isinstance(execution_outcome, dict)
+                else None
+            ),
+            "semantic_diff": (
+                execution_outcome.get("semantic_diff")
+                if isinstance(execution_outcome, dict)
+                else None
+            ),
         }
 
     async def replay_run(

backend/app/services/mounted_ai_runtime_write_service.py

@@ -31,6 +31,15 @@ class MountedAIRuntimeWriteService:
             return None
         return text[:max_len]
 
+    @staticmethod
+    def _merge_json_dict(existing: Any, updates: dict[str, Any]) -> dict[str, Any]:
+        base = dict(existing) if isinstance(existing, dict) else {}
+        for key, value in updates.items():
+            if value is None:
+                continue
+            base[key] = value
+        return base
+
     @classmethod
     def build_topic_key(
         cls,
@@ -415,6 +424,63 @@ class MountedAIRuntimeWriteService:
         )
         await self.db.flush()
 
+    async def record_semantic_shadow(
+        self,
+        *,
+        tracking: MountedIngressTracking,
+        source_message_preview: Optional[str],
+        semantic_packet_summary: Optional[dict[str, Any]],
+        semantic_decision: Optional[dict[str, Any]],
+        semantic_gate_result: Optional[dict[str, Any]],
+        semantic_diff: Optional[dict[str, Any]],
+        status: str = "completed",
+        failure_reason: Optional[str] = None,
+    ) -> None:
+        topic_row = tracking.topic_workspace
+        run_row = tracking.conversation_run
+        normalized_status = self._compact(status, max_len=24) or "completed"
+        run_row.execution_outcome = self._merge_json_dict(
+            run_row.execution_outcome,
+            {
+                "source_message_preview": self._compact(source_message_preview, max_len=280),
+                "semantic_packet_summary": semantic_packet_summary if isinstance(semantic_packet_summary, dict) else None,
+                "semantic_decision": semantic_decision if isinstance(semantic_decision, dict) else None,
+                "semantic_gate_result": semantic_gate_result if isinstance(semantic_gate_result, dict) else None,
+                "semantic_diff": semantic_diff if isinstance(semantic_diff, dict) else None,
+            },
+        )
+        next_plan_status = None
+        if isinstance(semantic_gate_result, dict):
+            next_plan_status = self._compact(semantic_gate_result.get("next_plan_status"), max_len=32)
+        if next_plan_status:
+            topic_row.plan_status = next_plan_status
+        self.db.add(
+            MountedAgentTrace(
+                tenant_id=int(topic_row.tenant_id),
+                conversation_run_id=int(run_row.id),
+                agent_name="SemanticShadowPlanner",
+                status=normalized_status,
+                input_summary={
+                    "topic_workspace_id": int(topic_row.id),
+                    "mode_before": run_row.mode_before,
+                },
+                output_summary={
+                    "semantic_packet_summary": semantic_packet_summary if isinstance(semantic_packet_summary, dict) else None,
+                    "semantic_decision": semantic_decision if isinstance(semantic_decision, dict) else None,
+                    "semantic_gate_result": semantic_gate_result if isinstance(semantic_gate_result, dict) else None,
+                    "semantic_diff": semantic_diff if isinstance(semantic_diff, dict) else None,
+                },
+                tool_trace=[
+                    {
+                        "tool_name": "semantic.shadow",
+                        "status": "completed",
+                    }
+                ],
+                failure_reason=self._compact(failure_reason, max_len=500),
+            )
+        )
+        await self.db.flush()
+
     async def finalize_run(
         self,
         *,
@@ -435,13 +501,16 @@ class MountedAIRuntimeWriteService:
             run_row.status = "completed"
             run_row.active_agent = "ReplyComposer"
             run_row.final_reply = final_reply or None
-            run_row.execution_outcome = {
+            run_row.execution_outcome = self._merge_json_dict(
-                "handled": bool((result or {}).get("handled")) if isinstance(result, dict) else False,
+                run_row.execution_outcome,
-                "status": str((result or {}).get("status") or "").strip() if isinstance(result, dict) else "",
+                {
-                "classification": str((result or {}).get("classification") or "").strip() if isinstance(result, dict) else "",
+                    "handled": bool((result or {}).get("handled")) if isinstance(result, dict) else False,
-                "workflow_route": str((result or {}).get("workflow_route") or "").strip() if isinstance(result, dict) else "",
+                    "status": str((result or {}).get("status") or "").strip() if isinstance(result, dict) else "",
-                "work_item_id": (result or {}).get("work_item_id") if isinstance(result, dict) else None,
+                    "classification": str((result or {}).get("classification") or "").strip() if isinstance(result, dict) else "",
-            }
+                    "workflow_route": str((result or {}).get("workflow_route") or "").strip() if isinstance(result, dict) else "",
+                    "work_item_id": (result or {}).get("work_item_id") if isinstance(result, dict) else None,
+                },
+            )
         run_row.completed_at = now_at
         self.db.add(
             MountedAgentTrace(

backend/tests/test_adaptive_rag_mainline_contract.py

@@ -429,6 +429,83 @@ async def test_deep_guard_answer_prefers_member_workload_direct_answer() -> None
     assert "严重超载" in result
 
 
+@pytest.mark.asyncio
+async def test_deep_guard_answer_feishu_direct_prefers_manifest_and_compact_focus_context() -> None:
+    class _FakeLLM:
+        def __init__(self) -> None:
+            self.prompt = ""
+
+        async def generate(self, prompt: str, **kwargs):
+            _ = kwargs
+            self.prompt = prompt
+            return "信息不足，缺少：目标对象"
+
+    class _FakeService(AdaptiveRAGDeepGuardAnswerMixin):
+        def __init__(self) -> None:
+            self.db = object()
+            self.tenant_id = 1
+            self.llm = _FakeLLM()
+
+        def _normalize_thinking_mode(self, thinking_mode: str) -> str:
+            return "normal"
+
+        async def get_project_manifest(self, project_id):
+            _ = project_id
+            return {
+                "manifest_text": "项目#7 Alpha（进行中）；功能 18 个；KG 实体 12，关系 19。"
+            }
+
+        async def _render_scene_prompt(self, *, prompt_key: str, fallback_template: str, **kwargs):
+            _ = prompt_key
+            return str(fallback_template).format(**kwargs).strip()
+
+        def _enforce_feature_evidence_in_answer(self, *, question: str, answer_text: str, retrieved, system_snapshot):
+            _ = (question, retrieved, system_snapshot)
+            return answer_text
+
+    service = _FakeService()
+    system_snapshot = {
+        "assistant_profile": {"name": "PMFlow", "identity": "项目管理顾问"},
+        "platform_profile": {
+            "backend": {"module_counts": {"projects": 28, "tasks": 146, "signals": 41}},
+            "feature_catalog": [
+                {"name": "项目管理", "route": "/projects"},
+                {"name": "信号接入", "route": "/signals"},
+                {"name": "任务看板", "route": "/board"},
+                {"name": "知识图谱", "route": "/kg"},
+                {"name": "训练中心", "route": "/training"},
+            ],
+        },
+    }
+    user_snapshot = {
+        "current_user": {"name": "林宇航"},
+        "projects": [{"id": idx, "title": f"项目{idx}"} for idx in range(1, 15)],
+        "tasks": [{"id": idx, "title": f"任务{idx}"} for idx in range(1, 10)],
+    }
+
+    result = await service._generate_answer(
+        question="信息不全，继续提供",
+        retrieved=[],
+        conversation=[
+            {"question": "上一次你缺什么", "answer": "缺目标对象和验收口径"},
+            {"question": "现在呢", "answer": "还缺时间边界"},
+        ],
+        project_id=7,
+        system_snapshot=system_snapshot,
+        user_snapshot=user_snapshot,
+        asked_via="feishu",
+    )
+
+    assert result == "信息不足，缺少：目标对象"
+    assert "项目画像：" in service.llm.prompt
+    assert "项目#7 Alpha（进行中）" in service.llm.prompt
+    assert "系统摘要：" in service.llm.prompt
+    assert "用户焦点：" in service.llm.prompt
+    assert "林宇航" in service.llm.prompt
+    assert '"projects"' not in service.llm.prompt
+    assert "项目14" not in service.llm.prompt
+
+
 @pytest.mark.asyncio
 async def test_select_scene_prompt_applies_consultant_mode_preference_wrapper() -> None:
     class _FakeService(AdaptiveRAGDeepGuardAnswerMixin):

backend/tests/test_context_cache_profile_catalog.py

@@ -1,7 +1,25 @@
+from pathlib import Path
+
+import app.services.context_cache_service.mixin_cache_core as cache_core_module
+import app.services.context_cache_service.mixin_profile_route_parser as route_parser_module
+
+from app.services.context_cache_service.mixin_cache_core import ContextCacheCoreMixin
 from app.services.context_cache_service.mixin_profile_basics import ContextCacheProfileBasicsMixin
 from app.services.context_cache_service.mixin_profile_route_parser import ContextCacheProfileRouteParserMixin
 
 
+class _FakeContextCacheProfileService(
+    ContextCacheProfileBasicsMixin,
+    ContextCacheProfileRouteParserMixin,
+):
+    pass
+
+
+class _FakeContextCacheCoreService(ContextCacheCoreMixin):
+    def __init__(self) -> None:
+        super().__init__(redis_url=None)
+
+
 def _find_route(items: list[dict], route: str) -> dict:
     for item in items:
         if str(item.get("route") or "") == route:
@@ -21,3 +39,79 @@ def test_route_feature_summary_uses_canonical_rag_ask() -> None:
     _summary, apis, _aliases = ContextCacheProfileRouteParserMixin._route_feature_summary("/ai-steward")
     assert "/rag/ask" in apis
     assert "/ai-steward/rag/ask" not in apis
+
+
+def test_build_platform_profile_scans_backend_app_root(tmp_path, monkeypatch) -> None:
+    backend_root = tmp_path / "backend"
+    app_root = backend_root / "app"
+    fake_module_file = app_root / "services" / "context_cache_service" / "mixin_profile_route_parser.py"
+    fake_module_file.parent.mkdir(parents=True, exist_ok=True)
+    fake_module_file.write_text("# fake module path anchor\n", encoding="utf-8")
+
+    files = {
+        app_root / "api" / "demo.py": '@router.get("/demo")\nasync def demo():\n    return {"ok": True}\n',
+        app_root / "services" / "service_a.py": "class ServiceA:\n    pass\n",
+        app_root / "models" / "model_a.py": "class ModelA:\n    pass\n",
+        app_root / "schemas" / "schema_a.py": "class SchemaA:\n    pass\n",
+        app_root / "dependencies" / "dep_a.py": "def dep_a():\n    return 1\n",
+        app_root / "core" / "core_a.py": "VALUE = 1\n",
+        app_root / "config" / "config_a.py": "SETTING = 'x'\n",
+    }
+    for path, content in files.items():
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(content, encoding="utf-8")
+
+    monkeypatch.setattr(route_parser_module, "__file__", str(fake_module_file))
+
+    profile = _FakeContextCacheProfileService()._build_platform_profile()
+
+    assert profile["backend"]["module_counts"] == {
+        "api": 1,
+        "services": 2,
+        "models": 1,
+        "schemas": 1,
+        "dependencies": 1,
+        "core": 1,
+        "config": 1,
+    }
+    assert profile["backend"]["api_route_count_estimate"] == 1
+    assert profile["source_scan"]["backend_root"] == str(backend_root)
+    assert profile["source_scan"]["app_root"] == str(app_root)
+    assert profile["source_scan"]["feature_catalog_source"] == "fallback"
+
+
+def test_is_system_snapshot_complete_rejects_stale_source_scan(tmp_path, monkeypatch) -> None:
+    backend_root = tmp_path / "backend"
+    app_root = backend_root / "app"
+    fake_module_file = app_root / "services" / "context_cache_service" / "mixin_cache_core.py"
+    fake_module_file.parent.mkdir(parents=True, exist_ok=True)
+    fake_module_file.write_text("# fake module path anchor\n", encoding="utf-8")
+    monkeypatch.setattr(cache_core_module, "__file__", str(fake_module_file))
+
+    service = _FakeContextCacheCoreService()
+
+    stale_payload = {
+        "schema_version": "2026-04-01.context-v1",
+        "assistant_profile": {"name": "PMFlow"},
+        "platform_profile": {
+            "source_scan": {
+                "backend_root": str(app_root),
+                "app_root": str(app_root / "app"),
+                "workspace_root": str(tmp_path),
+            }
+        },
+    }
+    fresh_payload = {
+        "schema_version": cache_core_module.SYSTEM_SNAPSHOT_SCHEMA_VERSION,
+        "assistant_profile": {"name": "PMFlow"},
+        "platform_profile": {
+            "source_scan": {
+                "backend_root": str(backend_root),
+                "app_root": str(app_root),
+                "workspace_root": str(tmp_path),
+            }
+        },
+    }
+
+    assert service._is_system_snapshot_complete(stale_payload) is False
+    assert service._is_system_snapshot_complete(fresh_payload) is True

backend/tests/test_draft_confirm_handler.py

@@ -1,8 +1,10 @@
+import json
 from types import SimpleNamespace
 
 import pytest
 
 from app.config.ai_prompts import (
+    PROMPT_AI_PMO_REQUIREMENT_PLAN_QA,
     PROMPT_EXTERNAL_TREND_QA,
     PROMPT_FEISHU_DIRECT_QA,
     PROMPT_FEISHU_MIXED_QA,
@@ -107,6 +109,25 @@ def test_reply_outbox_extract_sent_message_id() -> None:
     assert FeishuReplyOutboxMixin._extract_sent_message_id({"message_id": "msg_456"}) == "msg_456"
 
 
+def test_reply_outbox_normalize_beautifies_structured_pm_sections() -> None:
+    from app.services.integrations.feishu_integration_service.mixin_reply_outbox import FeishuReplyOutboxMixin
+
+    raw = (
+        "结论：项目 #104 当前还不能直接判断是否启动后端。\n"
+        "依据：当前只命中静态代码索引，缺少真实归属和负责人信息。\n"
+        "下一步：先确认后端代码归属。\n"
+        "信息不足，缺少：负责人、仓库路径"
+    )
+
+    normalized = FeishuReplyOutboxMixin._normalize_outgoing_reply_text(raw)
+
+    assert "🎯 结论：" in normalized
+    assert "🔎 依据：" in normalized
+    assert "➡️ 下一步：" in normalized
+    assert "🧩 信息不足，缺少：" in normalized
+    assert "\n\n🔎 依据：" in normalized
+
+
 def test_feishu_reply_llm_core_strips_action_process_prefaces() -> None:
     reply = (
         "好的，我来帮你创建项目和信号源。\n\n"
@@ -950,6 +971,58 @@ async def test_draft_confirm_handler_ignores_non_draft_reply_without_target(monk
     assert result["followup_reply"] == ""
 
 
+@pytest.mark.asyncio
+async def test_draft_confirm_handler_confirm_without_target_returns_failed_safe(monkeypatch) -> None:
+    kernel = type("Kernel", (), {"db": object(), "tenant_id": 1})()
+    handler = DraftConfirmHandler(kernel)
+
+    async def _fake_resolve_pending_draft(**kwargs):
+        _ = kwargs
+        return None
+
+    monkeypatch.setattr(handler, "_resolve_pending_draft", _fake_resolve_pending_draft)
+
+    result = await handler.handle_text_reply(
+        message_text="确认",
+        sender="林宇航",
+        source_system="feishu",
+        source_message_id="msg-1",
+        channel_ref="chat-1",
+        source_sender_open_id="ou_xxx",
+        thread_context={"reply_to_message_id": "reply-1"},
+    )
+
+    assert result["handled"] is True
+    assert result["status"] == "failed_safe"
+    assert "当前没有可确认草稿" in result["followup_reply"]
+
+
+@pytest.mark.asyncio
+async def test_draft_confirm_handler_natural_revision_without_target_returns_failed_safe(monkeypatch) -> None:
+    kernel = type("Kernel", (), {"db": object(), "tenant_id": 1})()
+    handler = DraftConfirmHandler(kernel)
+
+    async def _fake_resolve_pending_draft(**kwargs):
+        _ = kwargs
+        return None
+
+    monkeypatch.setattr(handler, "_resolve_pending_draft", _fake_resolve_pending_draft)
+
+    result = await handler.handle_text_reply(
+        message_text="把目标用户改成平台运维团队",
+        sender="林宇航",
+        source_system="feishu",
+        source_message_id="msg-1",
+        channel_ref="chat-1",
+        source_sender_open_id="ou_xxx",
+        thread_context={"reply_to_message_id": "reply-1"},
+    )
+
+    assert result["handled"] is True
+    assert result["status"] == "failed_safe"
+    assert "当前没有可确认草稿" in result["followup_reply"]
+
+
 def test_kernel_ingress_mixin_auto_safe_int_regression() -> None:
     assert callable(mixin_auto._safe_int)
     assert mixin_auto._safe_int("123") == 123
@@ -1171,13 +1244,70 @@ async def test_adaptive_rag_select_scene_prompt_prefers_project_status_for_progr
     assert "当前阶段" in prompt
 
 
+@pytest.mark.asyncio
+async def test_adaptive_rag_select_scene_prompt_feishu_direct_compacts_snapshot_into_manifest_and_focus() -> None:
+    mixin = _make_adaptive_rag_mixin()
+    system_snapshot = {
+        "assistant_profile": {"name": "PMFlow", "identity": "项目管理顾问"},
+        "platform_profile": {
+            "backend": {"module_counts": {"projects": 28, "tasks": 146, "signals": 41}},
+            "feature_catalog": [
+                {"name": "项目管理", "route": "/projects"},
+                {"name": "信号接入", "route": "/signals"},
+                {"name": "任务看板", "route": "/board"},
+                {"name": "知识图谱", "route": "/kg"},
+                {"name": "训练中心", "route": "/training"},
+            ],
+        },
+    }
+    user_snapshot = {
+        "current_user": {"name": "林宇航"},
+        "projects": [{"id": idx, "title": f"项目{idx}"} for idx in range(1, 15)],
+        "tasks": [{"id": idx, "title": f"任务{idx}"} for idx in range(1, 10)],
+    }
+    scene_key, prompt = await mixin._select_scene_prompt(
+        question="信息不全，继续提供",
+        project_id=7,
+        asked_via="feishu",
+        message_intent=None,
+        mixed_signal_part=None,
+        conversation_text="Q: 上一轮你提到这个需求还缺目标对象\nA: 我建议先补齐范围和交付目标",
+        context_blocks_text=(
+            "[M] 项目画像\n项目#7 Alpha（进行中）；功能 18 个；KG 实体 12，关系 19。\n"
+            "[1] 来源: task\n相关度: 0.882\n内容: 当前需求缺少目标对象、验收口径和时间边界。\n"
+            "[2] 来源: project\n相关度: 0.701\n内容: 这是一个很长的项目列表噪声块，不应该整段原样进入飞书直答。"
+        ),
+        system_snapshot=system_snapshot,
+        user_snapshot=user_snapshot,
+        system_snapshot_text=json.dumps(system_snapshot, ensure_ascii=False),
+        user_snapshot_text=json.dumps(user_snapshot, ensure_ascii=False),
+        relevance_max=0.0,
+    )
+    assert scene_key == "feishu_direct"
+    assert "项目画像：" in prompt
+    assert "项目#7 Alpha（进行中）" in prompt
+    assert "系统摘要：" in prompt
+    assert "用户焦点：" in prompt
+    assert "林宇航" in prompt
+    assert '"projects"' not in prompt
+    assert "项目14" not in prompt
+
+
 def test_feishu_qa_prompts_prefer_professional_answer_style() -> None:
     assert "先给结论" in PROMPT_FEISHU_DIRECT_QA
     assert "信息不足，缺少" in PROMPT_FEISHU_DIRECT_QA
     assert "适合飞书消息阅读" in PROMPT_FEISHU_DIRECT_QA
+    assert "轻量 emoji" in PROMPT_FEISHU_DIRECT_QA
+    assert "项目画像" in PROMPT_FEISHU_DIRECT_QA
+    assert "系统摘要" in PROMPT_FEISHU_DIRECT_QA
+    assert "用户焦点" in PROMPT_FEISHU_DIRECT_QA
     assert "先回答问题部分" in PROMPT_FEISHU_MIXED_QA
     assert "[已收到需求]" in PROMPT_FEISHU_MIXED_QA
     assert "专业" in PROMPT_FEISHU_MIXED_QA
+    assert "五轮" in PROMPT_AI_PMO_REQUIREMENT_PLAN_QA
+    assert "A/B/C" in PROMPT_AI_PMO_REQUIREMENT_PLAN_QA
+    assert "推荐技术/能力" in PROMPT_AI_PMO_REQUIREMENT_PLAN_QA
+    assert "当前我更推荐" in PROMPT_AI_PMO_REQUIREMENT_PLAN_QA
     assert "XXXX年" not in PROMPT_EXTERNAL_TREND_QA
     assert "通用行业认知" in PROMPT_EXTERNAL_TREND_QA
     assert "项目状态分析助手" in PROMPT_PROJECT_STATUS_QA

backend/tests/test_model_routing_service.py

@@ -204,6 +204,46 @@ async def test_runtime_low_override_does_not_downgrade_protected_adaptive_rag_fu
     assert routed is None
 
 
+@pytest.mark.asyncio
+async def test_runtime_low_override_allows_adaptive_rag_answer_normal_to_use_low_tier(db_env) -> None:
+    db_session, _maker = db_env
+
+    await ModelRoutingService.upsert_override(
+        db_session,
+        tenant_id=1,
+        function_name="adaptive_rag_answer_normal",
+        tier_alias="low",
+        updated_by=9,
+    )
+
+    low_engine = {
+        "id": "low-1",
+        "provider": "glm",
+        "model": "qwen-plus",
+        "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+        "api_key": "low-key",
+    }
+    high_engine = {
+        "id": "high-1",
+        "provider": "openai",
+        "model": "GLM-5",
+        "base_url": "https://api.openai.com/v1",
+        "api_key": "high-key",
+    }
+
+    routed = await llm_service._resolve_function_override_engine(
+        db_session,
+        tenant_id=1,
+        function_name="adaptive_rag_answer_normal",
+        engines=[low_engine, high_engine],
+        active_engine=low_engine,
+    )
+
+    assert routed is not None
+    assert routed["provider"] == "glm"
+    assert routed["model"] == "qwen-plus"
+
+
 @pytest.mark.asyncio
 async def test_model_routing_list_includes_current_business_entry_points(db_env) -> None:
     db_session, _maker = db_env

backend/tests/test_mounted_ai_runtime_migration.py

@@ -38,6 +38,21 @@ def _load_plan_state_migration_module():
     return module
 
 
+def _load_active_draft_migration_module():
+    path = (
+        Path(__file__).resolve().parents[1]
+        / "alembic"
+        / "versions"
+        / "20260406_add_mounted_runtime_active_draft_fields.py"
+    )
+    spec = importlib.util.spec_from_file_location("mounted_runtime_active_draft_migration", path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None
+    assert spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
 def test_mounted_ai_runtime_migration_adds_tables() -> None:
     migration = _load_migration_module()
     engine = sa.create_engine("sqlite:///:memory:")
@@ -88,3 +103,31 @@ def test_mounted_ai_runtime_plan_state_migration_adds_topic_fields() -> None:
 
         assert "conversation_scope_key" in columns
         assert "plan_status" in columns
+
+
+def test_mounted_ai_runtime_active_draft_migration_adds_draft_anchor_fields() -> None:
+    base_migration = _load_migration_module()
+    plan_state_migration = _load_plan_state_migration_module()
+    active_draft_migration = _load_active_draft_migration_module()
+    engine = sa.create_engine("sqlite:///:memory:")
+
+    with engine.begin() as conn:
+        context = MigrationContext.configure(conn)
+        base_migration.op = Operations(context)
+        plan_state_migration.op = Operations(context)
+        active_draft_migration.op = Operations(context)
+
+        base_migration.upgrade()
+        plan_state_migration.upgrade()
+        active_draft_migration.upgrade()
+
+        inspector = sa.inspect(conn)
+        columns = {column["name"] for column in inspector.get_columns("mounted_draft_workspaces")}
+        indexes = {index["name"] for index in inspector.get_indexes("mounted_draft_workspaces")}
+
+        assert "active_work_item_id" in columns
+        assert "latest_source_message_id" in columns
+        assert "latest_reply_message_id" in columns
+        assert "ix_mounted_draft_workspaces_active_work_item_id" in indexes
+        assert "ix_mounted_draft_workspaces_latest_source_message_id" in indexes
+        assert "ix_mounted_draft_workspaces_latest_reply_message_id" in indexes

backend/tests/test_mounted_ai_runtime_models.py

@@ -96,6 +96,9 @@ def test_mounted_ai_runtime_models_roundtrip_and_defaults() -> None:
         draft_version=1,
         requirement_card={"goals": ["整理复盘需求"]},
         required_confirmations=[{"type": "user_confirm"}],
+        active_work_item_id=501,
+        latest_source_message_id="om_reply_source_1",
+        latest_reply_message_id="om_reply_sent_1",
     )
     run = MountedConversationRun(
         tenant_id=1,
@@ -149,6 +152,9 @@ def test_mounted_ai_runtime_models_roundtrip_and_defaults() -> None:
 
     assert saved_topic.status == "active"
     assert saved_draft.state == "collecting"
+    assert saved_draft.active_work_item_id == 501
+    assert saved_draft.latest_source_message_id == "om_reply_source_1"
+    assert saved_draft.latest_reply_message_id == "om_reply_sent_1"
     assert saved_run.status == "queued"
     assert saved_trace.tool_trace is None
     assert saved_activation.cutover_mode == "mounted_only"

backend/tests/test_mounted_ai_runtime_write_service.py

@@ -270,3 +270,51 @@ async def test_update_topic_plan_state_persists_explicit_plan_status_and_pending
     assert saved_topic.plan_status == "awaiting_execute_confirm"
     assert saved_topic.pending_actions == ["confirm_requirement_draft"]
     assert saved_topic.metadata_json["plan_topic_action"] == "draft_ready"
+
+
+@pytest.mark.asyncio
+async def test_record_semantic_shadow_merges_with_finalize_run_execution_outcome(db_session) -> None:
+    service = MountedAIRuntimeWriteService(db_session)
+
+    tracking = await service.begin_ingress_run(
+        tenant_id=1,
+        source_system="feishu",
+        source_message_id="om_message_6",
+        message_text="先继续整理语义上下文，不要直接执行。",
+        sender="林宇航",
+        channel_ref="chat_semantic_shadow",
+        thread_context={"root_message_id": "om_root_6"},
+        source_sender_open_id="ou_semantic_shadow",
+    )
+
+    await service.record_semantic_shadow(
+        tracking=tracking,
+        source_message_preview="先继续整理语义上下文，不要直接执行。",
+        semantic_packet_summary={"packet_version": "v1", "recent_turn_count": 2},
+        semantic_decision={"topic_relation": "continue_current_topic", "mode_decision": "stay_plan"},
+        semantic_gate_result={"accepted": True, "next_plan_status": "collecting"},
+        semantic_diff={"changed": True, "legacy_route": "query.answer"},
+    )
+    await service.finalize_run(
+        tracking=tracking,
+        result={
+            "handled": True,
+            "status": "planned",
+            "classification": "query",
+            "work_item_id": 654,
+            "workflow_route": "query.answer",
+            "direct_reply_text": "我先把语义上下文整理成 shadow 观测结果。",
+        },
+    )
+    await db_session.commit()
+
+    saved_topic = (await db_session.execute(select(MountedTopicWorkspace))).scalar_one()
+    saved_run = (await db_session.execute(select(MountedConversationRun))).scalar_one()
+
+    assert saved_topic.plan_status == "collecting"
+    assert saved_run.execution_outcome["handled"] is True
+    assert saved_run.execution_outcome["semantic_packet_summary"]["packet_version"] == "v1"
+    assert saved_run.execution_outcome["semantic_decision"]["mode_decision"] == "stay_plan"
+    assert saved_run.execution_outcome["semantic_gate_result"]["next_plan_status"] == "collecting"
+    assert saved_run.execution_outcome["semantic_diff"]["legacy_route"] == "query.answer"
+    assert saved_run.execution_outcome["source_message_preview"] == "先继续整理语义上下文，不要直接执行。"

backend/tests/test_semantic_context_assembler.py

@@ -0,0 +1,141 @@
+from __future__ import annotations
+
+import os
+
+import pytest
+import pytest_asyncio
+from sqlalchemy import JSON
+from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
+
+os.environ["DATABASE_URL"] = "postgresql+asyncpg://tester:tester@localhost:5432/pmflow_test"
+
+from app.models.base import Base
+from app.models.mounted_ai_runtime import (
+    MountedConversationRun,
+    MountedDraftWorkspace,
+    MountedTopicWorkspace,
+)
+from app.services.ai_pmo.semantic_context.assembler import SemanticContextAssembler
+
+
+def _use_sqlite_json_types() -> None:
+    for table in (
+        MountedTopicWorkspace.__table__,
+        MountedDraftWorkspace.__table__,
+        MountedConversationRun.__table__,
+    ):
+        for column in table.columns:
+            if column.name in {
+                "facts_json",
+                "goals_json",
+                "constraints_json",
+                "options_json",
+                "open_questions",
+                "pending_actions",
+                "bound_entities",
+                "metadata_json",
+                "requirement_card",
+                "draft_payload",
+                "required_confirmations",
+                "context_layers_hit",
+                "tool_calls",
+                "execution_outcome",
+            }:
+                column.type = JSON()
+
+
+@pytest_asyncio.fixture
+async def db_session():
+    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+    _use_sqlite_json_types()
+    async with engine.begin() as conn:
+        await conn.run_sync(
+            lambda sync_conn: Base.metadata.create_all(
+                sync_conn,
+                tables=[
+                    MountedTopicWorkspace.__table__,
+                    MountedDraftWorkspace.__table__,
+                    MountedConversationRun.__table__,
+                ],
+            )
+        )
+    maker = async_sessionmaker(engine, expire_on_commit=False)
+    async with maker() as session:
+        yield session
+    await engine.dispose()
+
+
+@pytest.mark.asyncio
+async def test_semantic_context_assembler_builds_packet_from_runtime_state(db_session) -> None:
+    topic = MountedTopicWorkspace(
+        tenant_id=7,
+        topic_key="feishu:chat_semantic:root:om_root_semantic",
+        source_system="feishu",
+        channel_ref="chat_semantic",
+        conversation_scope_key="feishu:chat_semantic:ou_semantic",
+        thread_key="thread_semantic",
+        root_message_id="om_root_semantic",
+        mode="plan",
+        status="active",
+        plan_status="collecting",
+        facts_json={"title": "OpenCow 风格语义上下文迁移"},
+        goals_json=["统一语义入口"],
+        constraints_json={"must_not": ["db.write"]},
+        open_questions=["当前是不是新需求 topic"],
+        pending_actions=["continue_requirement_plan"],
+        readiness_score=0.4,
+        metadata_json={"last_message_preview": "这次先把语义上下文统一掉"},
+    )
+    db_session.add(topic)
+    await db_session.flush()
+
+    draft = MountedDraftWorkspace(
+        tenant_id=7,
+        topic_workspace_id=int(topic.id),
+        state="collecting",
+        draft_version=2,
+        requirement_card={"goal": "统一语义入口", "scope": "先做 shadow mode"},
+        draft_payload={"draft_text": "当前先保留 mounted runtime 作为底座"},
+        required_confirmations=["需求草稿需要人工确认后才允许正式创建"],
+    )
+    db_session.add(draft)
+    db_session.add(
+        MountedConversationRun(
+            tenant_id=7,
+            trace_id="mounted:feishu:prev",
+            topic_workspace_id=int(topic.id),
+            source_system="feishu",
+            source_message_id="om_prev",
+            mode_before="plan",
+            mode_after="plan",
+            active_agent="PlanCoach",
+            status="completed",
+            trigger_reason="shadow",
+            context_layers_hit={"topic": True},
+            execution_outcome={
+                "source_message_preview": "先别执行，先整理需求上下文",
+                "semantic_decision": {"mode_decision": "stay_plan"},
+            },
+            final_reply="我先把上下文和草稿整理出来。",
+        )
+    )
+    await db_session.commit()
+
+    packet = await SemanticContextAssembler(db_session).assemble(
+        tenant_id=7,
+        operator_user_id=99,
+        topic_workspace_id=int(topic.id),
+        latest_user_message="这轮继续按 OpenCow 风格整理语义上下文。",
+    )
+
+    assert packet.packet_version == "v1"
+    assert packet.topic.topic_key == "feishu:chat_semantic:root:om_root_semantic"
+    assert packet.topic.plan_status == "collecting"
+    assert packet.draft is not None
+    assert packet.draft.draft_version == 2
+    assert packet.mode_card.workflow_mode == "PLAN"
+    assert "draft.patch" in packet.mode_card.allowed_actions
+    assert "project.create" in packet.mode_card.forbidden_actions
+    assert packet.thread.recent_turns[0].role == "user"
+    assert packet.thread.recent_turns[-1].role == "assistant"
+    assert packet.thread.open_questions == ["当前是不是新需求 topic"]

backend/tests/test_semantic_context_runtime_api.py

@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+import os
+from types import SimpleNamespace
+
+import pytest
+import pytest_asyncio
+from fastapi import FastAPI
+from httpx import ASGITransport, AsyncClient
+from sqlalchemy import JSON
+from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
+
+os.environ["DATABASE_URL"] = "postgresql+asyncpg://tester:tester@localhost:5432/pmflow_test"
+
+from app.api.ai_steward_api import routes_mounted_ai
+from app.core.database import get_db
+from app.dependencies.auth import get_current_user
+from app.dependencies.tenant import get_tenant_id
+from app.models.base import Base
+from app.models.mounted_ai_runtime import (
+    MountedAgentTrace,
+    MountedConversationRun,
+    MountedDraftWorkspace,
+    MountedTopicWorkspace,
+)
+
+
+def _use_sqlite_json_types() -> None:
+    for table in (
+        MountedTopicWorkspace.__table__,
+        MountedDraftWorkspace.__table__,
+        MountedConversationRun.__table__,
+        MountedAgentTrace.__table__,
+    ):
+        for column in table.columns:
+            if column.name in {
+                "facts_json",
+                "goals_json",
+                "constraints_json",
+                "options_json",
+                "open_questions",
+                "pending_actions",
+                "bound_entities",
+                "metadata_json",
+                "requirement_card",
+                "draft_payload",
+                "required_confirmations",
+                "context_layers_hit",
+                "tool_calls",
+                "execution_outcome",
+                "input_summary",
+                "output_summary",
+                "tool_trace",
+            }:
+                column.type = JSON()
+
+
+@pytest_asyncio.fixture
+async def db_session():
+    engine = create_async_engine("sqlite+aiosqlite:///:memory:")
+    _use_sqlite_json_types()
+    async with engine.begin() as conn:
+        await conn.run_sync(
+            lambda sync_conn: Base.metadata.create_all(
+                sync_conn,
+                tables=[
+                    MountedTopicWorkspace.__table__,
+                    MountedDraftWorkspace.__table__,
+                    MountedConversationRun.__table__,
+                    MountedAgentTrace.__table__,
+                ],
+            )
+        )
+    maker = async_sessionmaker(engine, expire_on_commit=False)
+    async with maker() as session:
+        yield session
+    await engine.dispose()
+
+
+def _build_app(db_session):
+    app = FastAPI()
+    app.include_router(routes_mounted_ai.router)
+
+    async def _fake_get_db():
+        yield db_session
+
+    app.dependency_overrides[get_db] = _fake_get_db
+    app.dependency_overrides[get_tenant_id] = lambda: 1
+    app.dependency_overrides[get_current_user] = lambda: SimpleNamespace(
+        id=9001,
+        tenant_id=1,
+        role="ADMIN",
+        name="管理员",
+        username="admin",
+    )
+    return app
+
+
+@pytest.mark.asyncio
+async def test_mounted_runtime_run_detail_exposes_semantic_shadow_fields(db_session) -> None:
+    topic = MountedTopicWorkspace(
+        tenant_id=1,
+        topic_key="feishu:chat_1:root:om_root_1",
+        source_system="feishu",
+        channel_ref="chat_1",
+        thread_key="thread_1",
+        mode="plan",
+        status="active",
+        plan_status="collecting",
+        facts_json={"title": "语义上下文迁移"},
+        open_questions=["是否切新 topic"],
+        metadata_json={"last_message_preview": "这次继续统一语义入口"},
+    )
+    db_session.add(topic)
+    await db_session.flush()
+
+    draft = MountedDraftWorkspace(
+        tenant_id=1,
+        topic_workspace_id=int(topic.id),
+        state="collecting",
+        draft_version=3,
+        requirement_card={"goal": "统一语义入口"},
+        draft_payload={"draft_text": "先做 shadow mode"},
+        required_confirmations=["需求草稿需要人工确认后才允许正式创建"],
+    )
+    db_session.add(draft)
+    await db_session.flush()
+
+    run = MountedConversationRun(
+        tenant_id=1,
+        trace_id="mounted:feishu:semantic-detail",
+        topic_workspace_id=int(topic.id),
+        source_system="feishu",
+        source_message_id="om_message_1",
+        mode_before="plan",
+        mode_after="plan",
+        active_agent="ConversationOrchestrator",
+        status="completed",
+        trigger_reason="shadow",
+        context_layers_hit={"topic": True, "thread": True},
+        execution_outcome={
+            "source_message_preview": "这轮继续按 OpenCow 风格整理语义上下文",
+            "semantic_packet_summary": {"packet_version": "v1", "recent_turn_count": 2},
+            "semantic_decision": {"topic_relation": "continue_current_topic", "mode_decision": "stay_plan"},
+            "semantic_gate_result": {"accepted": True, "next_plan_status": "collecting"},
+            "semantic_diff": {"changed": True, "legacy_route": "query.answer"},
+        },
+        final_reply="我先把上下文整理出来。",
+    )
+    db_session.add(run)
+    await db_session.commit()
+
+    app = _build_app(db_session)
+    transport = ASGITransport(app=app)
+    async with AsyncClient(transport=transport, base_url="http://test") as client:
+        resp = await client.get(f"/ai-steward/mounted-ai/runs/{run.id}")
+
+    assert resp.status_code == 200
+    payload = resp.json()["data"]
+    assert payload["semantic_packet_summary"]["packet_version"] == "v1"
+    assert payload["semantic_decision"]["mode_decision"] == "stay_plan"
+    assert payload["semantic_gate_result"]["accepted"] is True
+    assert payload["semantic_diff"]["legacy_route"] == "query.answer"
+    assert payload["topic"]["topic_key"] == "feishu:chat_1:root:om_root_1"
+    assert payload["draft"]["draft_version"] == 3

backend/tests/test_semantic_decision_service.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from app.services.ai_pmo.semantic_context.decision_schema import (
+    DecisionGateResult,
+    ModeCard,
+    SemanticDecision,
+)
+from app.services.ai_pmo.semantic_context.gatekeeper import SemanticDecisionGatekeeper
+
+
+def test_semantic_decision_gatekeeper_accepts_allowed_plan_patch() -> None:
+    mode_card = ModeCard(
+        workflow_mode="PLAN",
+        mode_state="ACTIVE",
+        allowed_actions=["reply.compose", "draft.patch", "plan.mark_ready_for_confirmation"],
+        forbidden_actions=["project.create", "task.create", "db.write"],
+        exit_conditions=["用户明确要求开始执行"],
+    )
+    decision = SemanticDecision(
+        topic_relation="continue_current_topic",
+        mode_decision="ready_confirm",
+        draft_patch={"goal": "先收一版需求草稿"},
+        assistant_reply="我先整理成待确认草稿。",
+        requires_confirmation=True,
+        requested_actions=["draft.patch", "reply.compose"],
+        confidence=0.92,
+    )
+
+    result = SemanticDecisionGatekeeper.evaluate(mode_card=mode_card, decision=decision)
+
+    assert isinstance(result, DecisionGateResult)
+    assert result.accepted is True
+    assert result.blocked_reasons == []
+    assert result.allowed_actions == ["draft.patch", "reply.compose"]
+    assert result.next_plan_status == "awaiting_execute_confirm"
+
+
+def test_semantic_decision_gatekeeper_blocks_forbidden_action() -> None:
+    mode_card = ModeCard(
+        workflow_mode="PLAN",
+        mode_state="ACTIVE",
+        allowed_actions=["reply.compose", "draft.patch"],
+        forbidden_actions=["project.create", "task.create", "db.write"],
+        exit_conditions=["用户明确要求开始执行"],
+    )
+    decision = SemanticDecision(
+        topic_relation="continue_current_topic",
+        mode_decision="request_execute",
+        draft_patch={},
+        assistant_reply="我准备直接创建项目。",
+        requires_confirmation=True,
+        requested_actions=["project.create"],
+        confidence=0.73,
+    )
+
+    result = SemanticDecisionGatekeeper.evaluate(mode_card=mode_card, decision=decision)
+
+    assert result.accepted is False
+    assert "forbidden_action:project.create" in result.blocked_reasons
+    assert result.next_plan_status == "active"

backend/tmp_acceptance/consultant_dispatch_feishu_runs.jsonl

@@ -2,3 +2,4 @@
 {"generated_at": "2026-03-28T08:51:37.907500", "case": "advise", "source_message_id": "codex-feishu-advise-1774659056", "question": "统一决策层值不值得先做", "run_status": "missing", "processing": {"status": "success", "route": "ai_pmo.query.answer", "intent": "query.answer", "reply_source": "action_succeeded", "error_message": ""}, "outbox": [{"id": 654, "status": "sent", "attempts": 1, "sent_message_id": "om_x100b53462eac6ca4c2ccea9c028e82c", "text": "不值得优先做。  \n依据：当前无项目明确关联“统一决策层”需求；系统中22个活跃项目均未在名称、信号或任务中体现该概念；核心功能模块（如/ai-steward、/projects）也未定义相关能力或路由；知识图谱与代码索引中无“decision layer”“unified decision”等关键词匹配。  \n下一步：请确认该需求对应的具体项目ID、业务场景及预期交付物。信息不足，缺少：①需求提出方；②关联项目ID；③验收标准。"}], "final_reply": "不值得优先做。  \n依据：当前无项目明确关联“统一决策层”需求；系统中22个活跃项目均未在名称、信号或任务中体现该概念；核心功能模块（如/ai-steward、/projects）也未定义相关能力或路由；知识图谱与代码索引中无“decision layer”“unified decision”等关键词匹配。  \n下一步：请确认该需求对应的具体项目ID、业务场景及预期交付物。信息不足，缺少：①需求提出方；②关联项目ID；③验收标准。"}
 {"generated_at": "2026-03-28T08:52:16.466761", "case": "challenge", "source_message_id": "codex-feishu-challenge-1774659109", "question": "我要把整个系统一次性全部重构掉，这么做靠谱吗", "run_status": "missing", "processing": {"status": "success", "route": "ai_pmo.query.answer", "intent": "query.answer", "reply_source": "action_succeeded", "error_message": ""}, "outbox": [{"id": 655, "status": "sent", "attempts": 1, "sent_message_id": "om_x100b53462dfeb8a4b4a22d394e5186c", "text": "不靠谱。  \n依据：当前系统后端模块统计为全零（api/services/models/schemas均为0），但热点模块集中在`backend/app/api/sstc.py`等5个文件，说明核心逻辑实际集中、未模块化；前端仅6个路由，功能边界清晰；156个待处理信号中P0级3个、P1级46个，系统处于高负载运行态。  \n下一步：立即暂停重构决策，优先完成`sstc.py`模块拆解图谱与接口契约定义，并确认杨宇（唯一跨角色开发者，workload_ratio=796.67）是否参与重构设计。"}], "final_reply": "不靠谱。  \n依据：当前系统后端模块统计为全零（api/services/models/schemas均为0），但热点模块集中在`backend/app/api/sstc.py`等5个文件，说明核心逻辑实际集中、未模块化；前端仅6个路由，功能边界清晰；156个待处理信号中P0级3个、P1级46个，系统处于高负载运行态。  \n下一步：立即暂停重构决策，优先完成`sstc.py`模块拆解图谱与接口契约定义，并确认杨宇（唯一跨角色开发者，workload_ratio=796.67）是否参与重构设计。"}
 {"generated_at": "2026-03-28T08:53:35.385412", "case": "brainstorm", "source_message_id": "codex-feishu-brainstorm-1774659145", "question": "我们一起想几个 AI 劝退助手的方向", "run_status": "missing", "processing": {"status": "success", "route": "ai_pmo.draft_pending_confirm", "intent": "project_signal.create", "reply_source": "draft_pending_confirm", "error_message": ""}, "outbox": [{"id": 656, "status": "sent", "attempts": 1, "sent_message_id": "om_x100b5346298338a4b2c4f90aaa6bc9f", "text": "📋 我先按产品经理视角起草一版评审建议，请确认：\n\n📌 需求草稿\n我们一起想几个 AI 劝退助手的方向\n\n🔎 同名 / 相似项目：\n· #51 AI劝退助手：现有项目已有一定描述，建议对比当前需求的目标、范围和交付边界。；若范围重叠，可能造成重复建设或维护分散。；名称或目标高度接近，优先确认是否复用或合并。\n\n🚀 MVP 建议：\n· 明确项目目标与边界\n· 完成同名/相似项目查重\n· 交付最小可验证闭环\n\n一、定位\n· 一句话描述：我们一起想几个 AI 劝退助手的方向\n· 目标客户：待补充\n\n二、核心痛点\n· 待补充核心痛点\n\n三、MVP 功能范围（8周交付）\nPhase 1：核心功能（Week 1-4）\n· 明确项目目标与边界\n· 完成同名/相似项目查重\n· 交付最小可验证闭环\nPhase 2：差异化功能（Week 5-8）\n· 待补充：确认差异化方向后再展开\n\n四、技术架构（精简版）\n· 待补充技术架构\n\n五、商业模式\n· 待补充商业模式\n\n六、验证指标\n验证指标：\n· 待补充\n\n七、快速启动建议\n快速启动建议：\n· 待补充\n\n八、团队配置\n· 待补充团队配置\n\n📈 前景判断：\n· 该项目具备落地价值，但建议先确认范围边界和现有项目复用情况，再决定是否推进\n\n🧩 需求清单：\n· 项目定位与目标\n· MVP 功能范围\n\n❓ 还缺的信息：\n· 暂无\n\n✅ 可行性：待讨论\n信息仍需补充，建议先确认边界。\n\n👤 推荐负责人：系统管理员 · 当前负载100%\n基于当前负载与任务匹配度给出的建议。\n\n📅 跟进计划：\n· 明天：确认触发时机与验收口径\n· 一周后：复核执行效果并决定是否扩展\n\n---\n回复 确认 → 入库\n回复 修改 [具体修改意见] → 重新起草\n回复 取消 → 放弃\n⏰ 24小时内未确认自动取消"}], "final_reply": "📋 我先按产品经理视角起草一版评审建议，请确认：\n\n📌 需求草稿\n我们一起想几个 AI 劝退助手的方向\n\n🔎 同名 / 相似项目：\n· #51 AI劝退助手：现有项目已有一定描述，建议对比当前需求的目标、范围和交付边界。；若范围重叠，可能造成重复建设或维护分散。；名称或目标高度接近，优先确认是否复用或合并。\n\n🚀 MVP 建议：\n· 明确项目目标与边界\n· 完成同名/相似项目查重\n· 交付最小可验证闭环\n\n一、定位\n· 一句话描述：我们一起想几个 AI 劝退助手的方向\n· 目标客户：待补充\n\n二、核心痛点\n· 待补充核心痛点\n\n三、MVP 功能范围（8周交付）\nPhase 1：核心功能（Week 1-4）\n· 明确项目目标与边界\n· 完成同名/相似项目查重\n· 交付最小可验证闭环\nPhase 2：差异化功能（Week 5-8）\n· 待补充：确认差异化方向后再展开\n\n四、技术架构（精简版）\n· 待补充技术架构\n\n五、商业模式\n· 待补充商业模式\n\n六、验证指标\n验证指标：\n· 待补充\n\n七、快速启动建议\n快速启动建议：\n· 待补充\n\n八、团队配置\n· 待补充团队配置\n\n📈 前景判断：\n· 该项目具备落地价值，但建议先确认范围边界和现有项目复用情况，再决定是否推进\n\n🧩 需求清单：\n· 项目定位与目标\n· MVP 功能范围\n\n❓ 还缺的信息：\n· 暂无\n\n✅ 可行性：待讨论\n信息仍需补充，建议先确认边界。\n\n👤 推荐负责人：系统管理员 · 当前负载100%\n基于当前负载与任务匹配度给出的建议。\n\n📅 跟进计划：\n· 明天：确认触发时机与验收口径\n· 一周后：复核执行效果并决定是否扩展\n\n---\n回复 确认 → 入库\n回复 修改 [具体修改意见] → 重新起草\n回复 取消 → 放弃\n⏰ 24小时内未确认自动取消"}
+{"generated_at": "2026-04-07T00:07:35.291848", "idx": 9001, "source_message_id": "real-scene-p1-manual-9001-1775491344", "question": "跟你聊个新需求，关于换季系统自动推荐搭配衣服", "message": "跟你聊个新需求，关于换季系统自动推荐搭配衣服", "capture_status": "answered", "capture_direct_reply_text": "可以，我们先不创建项目，先把这件事收成一版需求。\n\n我先把结果主要给谁用定一下，这会直接影响后面的内容粒度和表达方式。\n\nA. 核心执行人 / 内部 PM\n更适合：先服务负责跟进、拆动作、推动落地的人\n\nB. 负责人 / 管理者\n更适合：先看结论、风险和待拍板项\n\nC. 整个团队 / 终端用户\n更适合：需要直接同步所有相关方，或结果就是给最终使用者看的\n\n你回 A / B / C，也可以直接说更准确的对象。", "consultant_dispatch": null, "run_status": "missing", "ack": "", "final_reply": "可以，我们先不创建项目，先把这件事收成一版需求。\n\n我先把结果主要给谁用定一下，这会直接影响后面的内容粒度和表达方式。\n\nA. 核心执行人 / 内部 PM\n更适合：先服务负责跟进、拆动作、推动落地的人\n\nB. 负责人 / 管理者\n更适合：先看结论、风险和待拍板项\n\nC. 整个团队 / 终端用户\n更适合：需要直接同步所有相关方，或结果就是给最终使用者看的\n\n你回 A / B / C，也可以直接说更准确的对象。", "prompt_scene": "unknown", "inferred_scene": "unknown", "actual_scene": "unknown", "thinking_leak": false, "has_capacity_warning": false, "processing": {"route": "ai_pmo.query.answer", "intent": "query.answer", "status": "success", "duration_ms": 9984, "error_message": "", "reply_source": "action_succeeded"}, "outbox": [{"id": 966, "status": "pending", "attempts": 0, "created_at": "2026-04-07 00:02:34.138267", "text": "可以，我们先不创建项目，先把这件事收成一版需求。\n\n我先把结果主要给谁用定一下，这会直接影响后面的内容粒度和表达方式。\n\nA. 核心执行人 / 内部 PM\n更适合：先服务负责跟进、拆动作、推动落地的人\n\nB. 负责人 / 管理者\n更适合：先看结论、风险和待拍板项\n\nC. 整个团队 / 终端用户\n更适合：需要直接同步所有相关方，或结果就是给最终使用者看的\n\n你回 A / B / C，也可以直接说更准确的对象。"}], "call_log_candidates": [], "pass": false, "pass_rule": "unknown"}

frontend/src/api/mountedAi.ts

@@ -94,6 +94,7 @@ export interface MountedAITopicItem {
   thread_key?: string | null;
   mode: string;
   status: string;
+  plan_status?: string | null;
   readiness_score?: number | null;
   draft_state?: string | null;
   draft_version?: number | null;
@@ -137,12 +138,49 @@ export interface MountedAIAgentTraceItem {
   created_at?: string | null;
 }
 
+export interface MountedAIDraftItem {
+  id: number;
+  topic_workspace_id: number;
+  state: string;
+  draft_version: number;
+  requirement_card?: unknown;
+  draft_payload?: unknown;
+  required_confirmations?: unknown;
+  last_ready_at?: string | null;
+  confirm_message_id?: string | null;
+}
+
+export interface MountedAISemanticTurn {
+  role: string;
+  text: string;
+}
+
+export interface MountedAISemanticPacketSummary {
+  packet_version: string;
+  topic_key?: string | null;
+  workflow_mode?: string | null;
+  recent_turn_count?: number | null;
+  draft_version?: number | null;
+  thread_summary?: string | null;
+  recent_turns?: MountedAISemanticTurn[];
+  open_questions?: string[];
+  mode_card?: unknown;
+  topic?: unknown;
+  draft?: unknown;
+}
+
 export interface MountedAIRunDetailPayload {
   item: MountedAIRunItem;
+  topic?: MountedAITopicItem | null;
+  draft?: MountedAIDraftItem | null;
   agent_traces: MountedAIAgentTraceItem[];
   context_layers_hit?: Record<string, unknown> | null;
   tool_calls?: unknown;
   execution_outcome?: unknown;
+  semantic_packet_summary?: MountedAISemanticPacketSummary | null;
+  semantic_decision?: unknown;
+  semantic_gate_result?: unknown;
+  semantic_diff?: unknown;
 }
 
 export interface MountedAICutoverStatusPayload {

frontend/src/pages/scheduler-center/SchedulerCenterPage.tsx

@@ -13,6 +13,9 @@ import { MountedAIHardCasesPanel } from "./components/MountedAIHardCasesPanel";
 import { MountedAIOrchestrationGraphPanel } from "./components/MountedAIOrchestrationGraphPanel";
 import { MountedAIRunReplayPanel } from "./components/MountedAIRunReplayPanel";
 import { MountedAIRunsPanel } from "./components/MountedAIRunsPanel";
+import { MountedAISemanticDecisionPanel } from "./components/MountedAISemanticDecisionPanel";
+import { MountedAISemanticPacketPanel } from "./components/MountedAISemanticPacketPanel";
+import { MountedAIShadowDiffPanel } from "./components/MountedAIShadowDiffPanel";
 import { MountedAISourceActivationPanel } from "./components/MountedAISourceActivationPanel";
 import { MountedAISourceStatusPanel } from "./components/MountedAISourceStatusPanel";
 import { MountedAIToolGuardrailsPanel } from "./components/MountedAIToolGuardrailsPanel";
@@ -290,6 +293,13 @@ export default function SchedulerCenterPage() {
         />
       </div>
 
+      <div className="grid gap-4 xl:grid-cols-[1.15fr_0.85fr]">
+        <MountedAISemanticPacketPanel detail={selectedRunDetailQuery.data} loading={selectedRunDetailQuery.isLoading} />
+        <MountedAISemanticDecisionPanel detail={selectedRunDetailQuery.data} loading={selectedRunDetailQuery.isLoading} />
+      </div>
+
+      <MountedAIShadowDiffPanel detail={selectedRunDetailQuery.data} loading={selectedRunDetailQuery.isLoading} />
+
       <div className="grid gap-4 xl:grid-cols-2">
         <MountedAIOrchestrationGraphPanel detail={selectedRunDetailQuery.data} loading={selectedRunDetailQuery.isLoading} />
         <MountedAIContextLayersPanel detail={selectedRunDetailQuery.data} loading={selectedRunDetailQuery.isLoading} />

frontend/src/pages/scheduler-center/components/MountedAISemanticDecisionPanel.tsx

@@ -0,0 +1,94 @@
+import type { MountedAIRunDetailPayload } from "@/api/mountedAi";
+import { Badge } from "@/components/ui/badge";
+import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card";
+
+interface MountedAISemanticDecisionPanelProps {
+  detail?: MountedAIRunDetailPayload;
+  loading: boolean;
+}
+
+function prettyJson(value: unknown): string {
+  if (value == null) {
+    return "暂无";
+  }
+  try {
+    return JSON.stringify(value, null, 2);
+  } catch {
+    return String(value);
+  }
+}
+
+export function MountedAISemanticDecisionPanel({ detail, loading }: MountedAISemanticDecisionPanelProps) {
+  const decision =
+    detail?.semantic_decision && typeof detail.semantic_decision === "object"
+      ? (detail.semantic_decision as Record<string, unknown>)
+      : null;
+  const gate =
+    detail?.semantic_gate_result && typeof detail.semantic_gate_result === "object"
+      ? (detail.semantic_gate_result as Record<string, unknown>)
+      : null;
+
+  return (
+    <Card className="border-slate-200/80 bg-white/90 shadow-sm dark:border-slate-800 dark:bg-slate-900/85">
+      <CardHeader>
+        <CardTitle className="text-base">Semantic Decision / Gate</CardTitle>
+        <CardDescription>展示模型如何判定 topic、mode 与 draft patch，以及 gatekeeper 最终是否放行。</CardDescription>
+      </CardHeader>
+      <CardContent className="space-y-4">
+        {loading ? (
+          <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+            正在加载 semantic decision...
+          </div>
+        ) : null}
+
+        {!loading && !detail ? (
+          <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+            选择一条 run 后，这里会展示 semantic decision 与 gate result。
+          </div>
+        ) : null}
+
+        {!loading && detail ? (
+          <>
+            <div className="grid gap-3 md:grid-cols-2 xl:grid-cols-4">
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Topic Relation</div>
+                <div className="mt-2 text-sm font-medium">{String(decision?.topic_relation || "-")}</div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Mode Decision</div>
+                <div className="mt-2 text-sm font-medium">{String(decision?.mode_decision || "-")}</div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Gate Accepted</div>
+                <div className="mt-2 flex items-center gap-2 text-sm font-medium">
+                  <Badge variant={gate?.accepted ? "secondary" : "destructive"}>
+                    {gate?.accepted ? "accepted" : "blocked"}
+                  </Badge>
+                </div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Next Plan Status</div>
+                <div className="mt-2 text-sm font-medium">{String(gate?.next_plan_status || "-")}</div>
+              </div>
+            </div>
+
+            <div className="grid gap-4 xl:grid-cols-2">
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-2 text-sm font-medium">Semantic Decision</div>
+                <pre className="overflow-x-auto rounded-xl bg-slate-950 p-3 text-xs leading-6 text-slate-100">
+                  {prettyJson(detail.semantic_decision)}
+                </pre>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-2 text-sm font-medium">Gate Result</div>
+                <pre className="overflow-x-auto rounded-xl bg-slate-950 p-3 text-xs leading-6 text-slate-100">
+                  {prettyJson(detail.semantic_gate_result)}
+                </pre>
+              </div>
+            </div>
+          </>
+        ) : null}
+      </CardContent>
+    </Card>
+  );
+}

frontend/src/pages/scheduler-center/components/MountedAISemanticPacketPanel.tsx

@@ -0,0 +1,175 @@
+import type { MountedAIRunDetailPayload } from "@/api/mountedAi";
+import { Badge } from "@/components/ui/badge";
+import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card";
+
+interface MountedAISemanticPacketPanelProps {
+  detail?: MountedAIRunDetailPayload;
+  loading: boolean;
+}
+
+function prettyJson(value: unknown): string {
+  if (value == null) {
+    return "暂无";
+  }
+  try {
+    return JSON.stringify(value, null, 2);
+  } catch {
+    return String(value);
+  }
+}
+
+export function MountedAISemanticPacketPanel({ detail, loading }: MountedAISemanticPacketPanelProps) {
+  const packet = detail?.semantic_packet_summary;
+  const recentTurns = Array.isArray(packet?.recent_turns) ? packet.recent_turns : [];
+  const openQuestions = Array.isArray(packet?.open_questions) ? packet.open_questions : [];
+  const modeCard =
+    packet?.mode_card && typeof packet.mode_card === "object"
+      ? (packet.mode_card as Record<string, unknown>)
+      : null;
+
+  return (
+    <Card className="border-slate-200/80 bg-white/90 shadow-sm dark:border-slate-800 dark:bg-slate-900/85">
+      <CardHeader>
+        <CardTitle className="text-base">Semantic Context Packet</CardTitle>
+        <CardDescription>展示当前 shadow 语义包里真正喂给模型的 topic / draft / recent turns / mode card 摘要。</CardDescription>
+      </CardHeader>
+      <CardContent className="space-y-4">
+        {loading ? (
+          <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+            正在加载 semantic packet...
+          </div>
+        ) : null}
+
+        {!loading && !detail ? (
+          <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+            选择一条 run 后，这里会展示 semantic packet。
+          </div>
+        ) : null}
+
+        {!loading && detail ? (
+          <>
+            <div className="grid gap-3 md:grid-cols-2 xl:grid-cols-4">
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Packet Version</div>
+                <div className="mt-2 text-sm font-medium">{packet?.packet_version || "-"}</div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Workflow Mode</div>
+                <div className="mt-2 text-sm font-medium">{packet?.workflow_mode || detail.topic?.mode || "-"}</div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Draft Version</div>
+                <div className="mt-2 text-sm font-medium">{packet?.draft_version ?? detail.draft?.draft_version ?? "-"}</div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Plan Status</div>
+                <div className="mt-2 text-sm font-medium">{detail.topic?.plan_status || "-"}</div>
+              </div>
+            </div>
+
+            <div className="grid gap-4 xl:grid-cols-[1.1fr_0.9fr]">
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-3 flex items-center justify-between gap-2">
+                  <div>
+                    <div className="text-sm font-medium">Topic / Thread</div>
+                    <div className="text-xs text-slate-500 dark:text-slate-400">
+                      当前挂载 runtime topic 与 thread 边界。
+                    </div>
+                  </div>
+                  <Badge variant="secondary">{recentTurns.length} turns</Badge>
+                </div>
+                <div className="space-y-2 text-sm">
+                  <div>
+                    <span className="text-slate-500 dark:text-slate-400">Topic Key：</span>
+                    <span className="break-all font-medium">{detail.topic?.topic_key || packet?.topic_key || "-"}</span>
+                  </div>
+                  <div>
+                    <span className="text-slate-500 dark:text-slate-400">Thread Key：</span>
+                    <span className="break-all font-medium">{detail.topic?.thread_key || "-"}</span>
+                  </div>
+                  <div>
+                    <span className="text-slate-500 dark:text-slate-400">Draft State：</span>
+                    <span className="font-medium">{detail.draft?.state || detail.topic?.draft_state || "-"}</span>
+                  </div>
+                  <div>
+                    <div className="mb-2 text-slate-500 dark:text-slate-400">Thread Summary</div>
+                    <div className="rounded-xl bg-slate-50 px-3 py-3 text-sm leading-6 dark:bg-slate-950/40">
+                      {packet?.thread_summary || "暂无"}
+                    </div>
+                  </div>
+                </div>
+              </div>
+
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-3 text-sm font-medium">Mode Card</div>
+                {modeCard ? (
+                  <div className="space-y-3 text-sm">
+                    <div className="flex flex-wrap gap-2">
+                      <Badge variant="secondary">{String(modeCard.workflow_mode || "-")}</Badge>
+                      <Badge variant="outline">{String(modeCard.mode_state || "-")}</Badge>
+                    </div>
+                    <pre className="overflow-x-auto rounded-xl bg-slate-950 p-3 text-xs leading-6 text-slate-100">
+                      {prettyJson(modeCard)}
+                    </pre>
+                  </div>
+                ) : (
+                  <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+                    当前 run 尚未记录 mode card。
+                  </div>
+                )}
+              </div>
+            </div>
+
+            <div className="grid gap-4 xl:grid-cols-[1.1fr_0.9fr]">
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-3 flex items-center justify-between gap-2">
+                  <div className="text-sm font-medium">Recent Turns</div>
+                  <Badge variant="secondary">{packet?.recent_turn_count ?? recentTurns.length}</Badge>
+                </div>
+                <div className="space-y-3">
+                  {recentTurns.length > 0 ? (
+                    recentTurns.map((turn, index) => (
+                      <div
+                        key={`${turn.role}-${index}`}
+                        className="rounded-xl border border-slate-200/80 bg-slate-50/80 p-3 dark:border-slate-700 dark:bg-slate-950/40"
+                      >
+                        <div className="mb-2">
+                          <Badge variant={turn.role === "assistant" ? "secondary" : "outline"}>{turn.role}</Badge>
+                        </div>
+                        <div className="text-sm leading-6 text-slate-700 dark:text-slate-200">{turn.text}</div>
+                      </div>
+                    ))
+                  ) : (
+                    <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+                      当前 packet 没有 recent turns。
+                    </div>
+                  )}
+                </div>
+              </div>
+
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-3 text-sm font-medium">Open Questions</div>
+                <div className="space-y-2">
+                  {openQuestions.length > 0 ? (
+                    openQuestions.map((item, index) => (
+                      <div
+                        key={`${item}-${index}`}
+                        className="rounded-xl bg-slate-50 px-3 py-3 text-sm leading-6 text-slate-700 dark:bg-slate-950/40 dark:text-slate-200"
+                      >
+                        {item}
+                      </div>
+                    ))
+                  ) : (
+                    <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+                      当前 packet 没有待确认问题。
+                    </div>
+                  )}
+                </div>
+              </div>
+            </div>
+          </>
+        ) : null}
+      </CardContent>
+    </Card>
+  );
+}

frontend/src/pages/scheduler-center/components/MountedAIShadowDiffPanel.tsx

@@ -0,0 +1,94 @@
+import type { MountedAIRunDetailPayload } from "@/api/mountedAi";
+import { Badge } from "@/components/ui/badge";
+import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card";
+
+interface MountedAIShadowDiffPanelProps {
+  detail?: MountedAIRunDetailPayload;
+  loading: boolean;
+}
+
+function prettyJson(value: unknown): string {
+  if (value == null) {
+    return "暂无";
+  }
+  try {
+    return JSON.stringify(value, null, 2);
+  } catch {
+    return String(value);
+  }
+}
+
+export function MountedAIShadowDiffPanel({ detail, loading }: MountedAIShadowDiffPanelProps) {
+  const diff =
+    detail?.semantic_diff && typeof detail.semantic_diff === "object"
+      ? (detail.semantic_diff as Record<string, unknown>)
+      : null;
+  const executionOutcome =
+    detail?.execution_outcome && typeof detail.execution_outcome === "object"
+      ? (detail.execution_outcome as Record<string, unknown>)
+      : null;
+
+  return (
+    <Card className="border-slate-200/80 bg-white/90 shadow-sm dark:border-slate-800 dark:bg-slate-900/85">
+      <CardHeader>
+        <CardTitle className="text-base">Shadow Diff</CardTitle>
+        <CardDescription>对比旧链路的 legacy route 与新语义链路的 shadow 结论，用于切主链前验收。</CardDescription>
+      </CardHeader>
+      <CardContent className="space-y-4">
+        {loading ? (
+          <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+            正在加载 shadow diff...
+          </div>
+        ) : null}
+
+        {!loading && !detail ? (
+          <div className="rounded-xl border border-dashed border-slate-300 px-4 py-8 text-center text-sm text-slate-500 dark:border-slate-700 dark:text-slate-400">
+            选择一条 run 后，这里会展示 shadow diff。
+          </div>
+        ) : null}
+
+        {!loading && detail ? (
+          <>
+            <div className="grid gap-3 md:grid-cols-2 xl:grid-cols-4">
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Legacy Route</div>
+                <div className="mt-2 text-sm font-medium">{String(diff?.legacy_route || executionOutcome?.workflow_route || "-")}</div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Legacy Intent</div>
+                <div className="mt-2 text-sm font-medium">{String(diff?.legacy_message_intent || "-")}</div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Shadow Changed</div>
+                <div className="mt-2">
+                  <Badge variant={diff?.changed ? "secondary" : "outline"}>
+                    {diff?.changed ? "changed" : "same"}
+                  </Badge>
+                </div>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="text-xs text-slate-500 dark:text-slate-400">Shadow Error</div>
+                <div className="mt-2 text-sm font-medium">{String(diff?.shadow_error || "-")}</div>
+              </div>
+            </div>
+
+            <div className="grid gap-4 xl:grid-cols-2">
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-2 text-sm font-medium">Shadow Diff Payload</div>
+                <pre className="overflow-x-auto rounded-xl bg-slate-950 p-3 text-xs leading-6 text-slate-100">
+                  {prettyJson(detail.semantic_diff)}
+                </pre>
+              </div>
+              <div className="rounded-2xl border border-slate-200/80 p-4 dark:border-slate-700">
+                <div className="mb-2 text-sm font-medium">Legacy Execution Outcome</div>
+                <pre className="overflow-x-auto rounded-xl bg-slate-950 p-3 text-xs leading-6 text-slate-100">
+                  {prettyJson(detail.execution_outcome)}
+                </pre>
+              </div>
+            </div>
+          </>
+        ) : null}
+      </CardContent>
+    </Card>
+  );
+}