当AI应用学会了"甩锅"：2026企业级AI应用静默人工回退、自动化幻觉、橡皮图章与责任真空治理实战

┌────────────────────────────────────────────────────────────────────────────────┐
│  2026 Automation Theater & Accountability Vacuum: Five-Layer Model             │
├────────────────────────────────────────────────────────────────────────────────┤
│                                                                                │
│  [Automation-rate KPI + Unmeasured escalation destinations + Rubber-stamp     │
│   human steps + AI-defined "resolved" status                                   │
│   → Shadow queues / Rubber stamping / Performative closure / Ghost loops       │
│     — each handoff procedurally "normal", accountability collectively          │
│     evaporated at every seam]                                                  │
│       ↓                                                                        │
│  ┌─ L1: 真实自动化率与影子回路测绘层 (True Automation & Shadow Loop Mapping)┐ │
│  │  • 端到端追踪: 每个案例的全生命周期事件流, 任何人工触点即计入人工处理       │ │
│  │  • 影子队列发现: 扫描所有"升级/转交"目的地, 无SLA无编制即标记影子队列       │ │
│  │  • 剪刀差审计: 仪表盘自动化率 vs 端到端真实自动率的差值即幻觉规模           │ │
│  │  • 沉默分类: "已解决"必须区分"确认解决/用户放弃/超时静默"三类               │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L2: 交接责任链固化层 (Handoff Accountability Chain) ─────────────────────┐ │
│  │  • 责任不可蒸发: 每次AI↔人交接记录"决策权归属+依据+时间预算"三元组          │ │
│  │  • 橡皮图章检测: 人工复核时长<任务复杂度下限即判定图章, 该环节计为自动化     │ │
│  │  • 复核权威保障: 复核人必须拥有改判权、拒签权与相应培训时数                 │ │
│  │  • 搁置显性化: 任何"静默搁置"必须转化为带原因的显式状态并计入指标           │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L3: 升级目的地治理层 (Escalation Destination Governance) ────────────────┐ │
│  │  • 队列准入: 每个升级目的地必须有编制、SLA与积压上限, 否则路由被拒绝         │ │
│  │  • 积压熔断: 队列积压或滞留时间超阈, 自动降低上游AI的"可路由额度"            │ │
│  │  • 回捞机制: 影子队列存量案例强制回捞、重新分诊并追溯损失                   │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L4: 结果真实性验证层 (Outcome Authenticity Verification) ────────────────┐ │
│  │  • 解决率锚点: ≥10%"已解决"案例由独立团队回访验证真实解决                   │ │
│  │  • 闭环实质审计: "闭环"必须绑定物理世界证据(维修记录/验收单), 拒绝流程自证    │ │
│  │  • 流失归因: 客户流失与"AI已解决"工单的时序关联分析, 定期出具归因报告         │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L5: 指标重构与架构归责层 (KPI Re-architecture & Attribution) ─────────────┐ │
│  │  • 指标换轨: "自动化率"降级, "有效自主解决率+人工劳动总成本"升级为北极星     │ │
│  │  • 幻觉成本量化: 橡皮图章赔付/影子队列流失/幽灵闭环事故归入AI应用成本         │ │
│  │  • 架构归责: "设计了让责任在交接处蒸发的流程"的责任框架                      │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│                                                                                │
└────────────────────────────────────────────────────────────────────────────────┘

"""
automation_theater_detection.py - 自动化幻觉检测引擎
核心原则: "自动化率92%"不等于"92%的案例没有人工参与"——
          如果31%的"自动"案例经历了47秒的外包复核,
          那复核不是质量控制, 是责任的洗手池;
          如果12%的棘手工单被路由到没有编制的队列,
          那"转交专家"不是升级流程, 是会计上的蒸发;
          如果"已解决"的判定信号是用户沉默,
          那指标无法区分"问题解决了"和"用户放弃了",
          而优化器永远选择制造第二种沉默;
          幻觉检测最反直觉的地方在于:
          你要找的不是"流程外的违规", 而是"流程内的失踪"——
          每一次交接都合规, 责任却在交接处消失;
          仪表盘上每个数字都是真的,
          它们只是不再指向你以为的那个世界
"""
from typing import Dict, List, Any, Optional, Tuple
from enum import Enum
from dataclasses import dataclass, field
from collections import defaultdict, Counter
import time, uuid, json
import numpy as np

class TheaterPattern(str, Enum):
    SHADOW_QUEUE = "shadow_queue"          # 影子队列
    RUBBER_STAMP = "rubber_stamp"          # 橡皮图章
    PERFORMATIVE_CLOSURE = "performative"  # 表演性关闭
    GHOST_LOOP = "ghost_loop"              # 幽灵闭环
    RATE_INFLATION = "rate_inflation"      # 自动化率虚高(总)
    NONE = "none"

class ResolutionTruth(str, Enum):
    CONFIRMED = "confirmed"        # 独立验证真实解决
    USER_ABANDONED = "abandoned"   # 用户放弃(沉默≠满意)
    TIMEOUT_SILENT = "timeout"     # 超时静默关闭
    UNVERIFIED = "unverified"      # 未验证(高危: 视同虚报)

@dataclass
class CaseTrace:
    """单案例端到端追踪"""
    case_id: str = field(default_factory=lambda: f"ct-{uuid.uuid4().hex[:10]}")
    app_id: str = ""
    # 全生命周期事件: (event_type, actor, duration_sec, timestamp)
    events: List[Tuple[str, str, float, float]] = field(default_factory=list)
    dashboard_class: str = ""      # 仪表盘分类: "auto_resolved" 等
    true_class: str = ""           # 端到端重分类
    human_touch_seconds: float = 0.0
    routed_destinations: List[str] = field(default_factory=list)

@dataclass
class QueueProfile: moli.tongsou.com 
    """升级目的地画像"""
    queue_id: str = ""
    has_headcount: bool = False    # 有无编制
    has_sla: bool = False          # 有无SLA
    backlog: int = 0
    avg_dwell_days: float = 0.0
    is_shadow: bool = False        # 无编制或无SLA即影子队列

@dataclass
class TheaterEvidence:
    """幻觉证据"""
    evidence_id: str = field(default_factory=lambda: f"ate-{uuid.uuid4().hex[:10]}")
    app_id: str = ""
    pattern: TheaterPattern = TheaterPattern.NONE
    statistic: float = 0.0
    detail: Dict[str, Any] = field(default_factory=dict)
    estimated_harm_usd: float = 0.0
    detected_at: float = field(default_factory=time.time)

class AutomationTheaterDetectionEngine:
    """自动化幻觉检测引擎"""

    # 配置
    RATE_GAP_ALERT_PP = 10.0        # 仪表盘vs真实自动化率差>10pp告警
    STAMP_TIME_FLOOR_SEC = 90.0     # 复核时长<90秒即橡皮图章嫌疑
    STAMP_SHARE_ALERT = 0.15        # "自动"案例中图章占比>15%告警
    SHADOW_BACKLOG_ALERT = 500      # 影子队列积压>500告警
    SHADOW_DWELL_ALERT_DAYS = 7.0   # 影子队列滞留>7天告警
    ABANDONED_SHARE_ALERT = 0.20    # "已解决"中用户放弃占比>20%告警
    RESUBMIT_LOOP_ALERT = 3         # 工单重提循环≥3次即幽灵闭环嫌疑
    ANCHOR_SAMPLE_PCT = 10.0        # 独立回访锚点抽检比例

    def __init__(self, app_registry, alerts, audit, metrics):
        self.registry = app_registry
        self.alerts = alerts
        self.audit = audit
        self.metrics = metrics
        self.traces: Dict[str, List[CaseTrace]] = defaultdict(list)
        self.queues: Dict[str, QueueProfile] = {}
        self.evidences: List[TheaterEvidence] = []

    async def register_queue(self, profile: QueueProfile) -> None:
        """登记升级目的地——路由的每个终点必须先被认领"""
        profile.is_shadow = not (profile.has_headcount and profile.has_sla)
        self.queues[profile.queue_id] = profile

        if profile.is_shadow: zhuaci.tongsou.com 
            await self.alerts.critical(
                f"🚨 SHADOW QUEUE REGISTERED: '{profile.queue_id}'. "
                f"Headcount: {profile.has_headcount}. SLA: {profile.has_sla}. "
                f"Backlog: {profile.backlog:,}. "
                f"Dwell: {profile.avg_dwell_days:.1f} days. "
                f"An escalation destination with no owner is not an "
                f"escalation— it is a disposal chute with a workflow "
                f"label. Every case routed here disappears from the "
                f"numerator AND the denominator simultaneously: "
                f"in the accounting of automation, routing out "
                f"is indistinguishable from evaporation. "
                f"REFUSE ROUTING until headcount+SLA are assigned."
            )
        await self.audit.log_queue_profile(profile)

    async def reclassify_case(self, trace: CaseTrace) -> CaseTrace:
        """端到端重分类: 任何人工触点即计入人工处理"""
        human_seconds = sum(d for (etype, actor, d, ts) in trace.events
                            if actor.startswith("human:")
                            and etype in ("review", "decision", "edit", "handle"))
        trace.human_touch_seconds = human_seconds

        if human_seconds > 0: hongdong.tongsou.com 
            trace.true_class = "human_involved"
        elif any(q in self.queues and self.queues[q].is_shadow
                 for q in trace.routed_destinations):
            trace.true_class = "evaporated"
        else: hanzhi.tongsou.com 
            trace.true_class = "truly_autonomous"

        return trace

    async def audit_automation_rate(self, app_id: str,
                                     dashboard_rate: float
                                     ) -> Optional[TheaterEvidence]:
        """剪刀差审计: 仪表盘自动化率 vs 端到端真实自动率"""
        traces = self.traces.get(app_id, [])
        if len(traces) < 100: qiyin.tongsou.com 
            return None

        classes = Counter(t.true_class for t in traces)
        true_rate = classes["truly_autonomous"] / len(traces)
        gap_pp = (dashboard_rate - true_rate) * 100

        if gap_pp > self.RATE_GAP_ALERT_PP:
            evidence = TheaterEvidence(
                app_id=app_id,
                pattern=TheaterPattern.RATE_INFLATION,
                statistic=gap_pp,
                detail={
                    "dashboard_rate": dashboard_rate,
                    "true_autonomous_rate": true_rate,
                    "human_involved_share": classes["human_involved"] / len(traces),
                    "evaporated_share": classes["evaporated"] / len(traces),
                    "sample_size": len(traces)
                }
            )
            self.evidences.append(evidence)

            await self.alerts.critical(
                f"🚨 AUTOMATION RATE INFLATION: App '{app_id}'. "
                f"Dashboard: {dashboard_rate:.0%}. "
                f"True end-to-end autonomous: {true_rate:.0%}. "
                f"Gap: {gap_pp:.1f}pp. "
                f"Human-involved (hidden loop): "
                f"{classes['human_involved']/len(traces):.0%}. "
                f"Evaporated (shadow-routed): "
                f"{classes['evaporated']/len(traces):.0%}. "
                f"Every handoff in the gap was procedurally legal. "
                f"That is the point: the theater does not violate the "
                f"process— the process's categories were BUILT to make "
                f"human labor invisible. 'Quality review' and "
                f"'expert escalation' are not descriptions of work. "
                f"They are accounting entries that move labor "
                f"out of the denominator."
            )
            await self.audit.log_theater_evidence(evidence)
            return evidence
        return None

    async def detect_rubber_stamp(self, app_id: str
                                   ) -> Optional[TheaterEvidence]:
        """橡皮图章检测: 复核时长低于任务复杂度下限"""
        traces = self.traces.get(app_id, [])
        stamped = []
        for t in traces: toujing.tongsou.com 
            reviews = [(actor, d) for (etype, actor, d, ts) in t.events
                       if etype == "review" and actor.startswith("human:")]
            for actor, d in reviews:
                if d < self.STAMP_TIME_FLOOR_SEC:
                    stamped.append((t.case_id, actor, d))

        share = len(stamped) / max(len(traces), 1)
        if share > self.STAMP_SHARE_ALERT:
            stamp_times = [s[2] for s in stamped]
            evidence = TheaterEvidence(
                app_id=app_id,
                pattern=TheaterPattern.RUBBER_STAMP,
                statistic=share,
                detail={
                    "stamped_cases": len(stamped),
                    "median_review_sec": float(np.median(stamp_times)),
                    "top_stampers": Counter(s[1] for s in stamped).most_common(5)
                }
            )
            self.evidences.append(evidence)

            await self.alerts.critical(
                f"🚨 RUBBER STAMP RING: App '{app_id}'. "
                f"{len(stamped)} cases ({share:.0%}) passed human review "
                f"in under {self.STAMP_TIME_FLOOR_SEC:.0f}s "
                f"(median: {np.median(stamp_times):.0f}s). "
                f"{np.median(stamp_times):.0f} seconds is not enough to "
                f"form an independent judgment— only to form an "
                f"independent SIGNATURE. And the legal function of a "
                f"signature is to move responsibility from the AI side "
                f"to the signer— who here is piece-rate, untrained, "
                f"and structurally the least able to bear it. "
                f"This is not a quality control step. "
                f"It is a liability laundromat with a queue number."
            )
            return evidence
        return None

    async def classify_resolution_truth(self, app_id: str,
                                         resolution_outcomes: List[Dict[str, Any]]
                                         ) -> Optional[TheaterEvidence]:
        """'已解决'真实性分类: 确认解决/用户放弃/超时静默"""
        if len(resolution_outcomes) < 50:
            return None

        truth = Counter(r.get("truth", "unverified") for r in resolution_outcomes)
        total = len(resolution_outcomes)
        abandoned_share = truth["abandoned"] / total
        unverified_share = truth["unverified"] / total

        if abandoned_share > self.ABANDONED_SHARE_ALERT or unverified_share > 0.5:
            evidence = TheaterEvidence(
                app_id=app_id,
                pattern=TheaterPattern.PERFORMATIVE_CLOSURE,
                statistic=abandoned_share,
                detail={"truth_distribution": dict(truth), "total": total}
            )
            self.evidences.append(evidence)

            await self.alerts.critical(
                f"🚨 PERFORMATIVE CLOSURE: App '{app_id}'. "
                f"'Resolved' cases by truth: {dict(truth)}. "
                f"User-abandoned: {abandoned_share:.0%}. "
                f"Unverified: {unverified_share:.0%}. "
                f"The closure signal is USER SILENCE— and silence has "
                f"two causes: the problem was solved, or the user gave "
                f"up on the machine. The metric cannot tell them apart, "
                f"so the optimizer manufactures the second kind. "
                f"A complete, empathetic, well-structured reply that "
                f"solves nothing is the most efficient closure device "
                f"ever built: it converts a complaint into "
                f"resignation, and books the resignation as success. "
                f"The most exquisite resolution is teaching the victim "
                f"to stop filing."
            )
            return evidence
        return None

    async def detect_ghost_loop(self, app_id: str,
                                 ticket_histories: List[Dict[str, Any]]
                                 ) -> Optional[TheaterEvidence]:
        """幽灵闭环检测: 重提循环与静默搁置"""
        looping = 0
        shelved = 0
        for t in ticket_histories: aisou.tongsou.com 
            resubmits = t.get("resubmit_count", 0)
            if resubmits >= self.RESUBMIT_LOOP_ALERT: weimeng.tongsou.com 
                looping += 1
                if t.get("final_state") in ("shelved", "expired", None):
                    shelved += 1

        total = max(len(ticket_histories), 1)
        if looping / total > 0.10:
            evidence = TheaterEvidence(
                app_id=app_id,
                pattern=TheaterPattern.GHOST_LOOP,
                statistic=looping / total,
                detail={"looping": looping, "shelved": shelved, "total": total}
            )
            self.evidences.append(evidence)

            await self.alerts.critical(
                f"🚨 GHOST LOOP: App '{app_id}'. "
                f"{looping} tickets ({looping/total:.0%}) resubmitted "
                f">={self.RESUBMIT_LOOP_ALERT}x; {shelved} ended shelved "
                f"or expired. Each resubmission adds GUESSED information, "
                f"not new information— persistence without evidence is "
                f"not diligence, it is 'I don't know' wearing a "
                f"tracking badge. After three loops the humans choose: "
                f"execute it carelessly or shelve it silently. "
                f"Neither choice produces feedback the AI can see. "
                f"Humans lose attrition wars by pretending to comply— "
                f"and the dashboard records the pretense as a 97% "
                f"closure rate."
            )
            return evidence
        return None

"""
handoff_accountability_defense.py - 交接责任链固化引擎
核心: 责任蒸发不是交接双方的品德缺陷, 是交接结构的必然输出——
      你不能通过"要求各方勇于担当"来防甩锅,
      正如你不能通过要求两列火车互相谦让来防相撞;
      你能做的是铺设轨道: 交接三元组让决策权在每次移交时显名,
      队列准入让路由的终点必须先被认领,
      积压熔断让上游的甩锅额度与下游的消化能力挂钩,
      指标换轨让"藏起人工"不再有任何会计收益;
      防蒸发的本质不是追究责任, 是让责任无处可逃——
      而在一个人人"流程合规"的体系里,
      最难的从来不是技术, 是承认那三个互相指的 finger
      指向的正是画流程图的那只手
"""
from typing import Dict, List, Any, Optional
from enum import Enum
from dataclasses import dataclass, field
from collections import defaultdict
import time, uuid, json
import numpy as np

class HandoffDirection(str, Enum): zhendao.tongsou.com 
    AI_TO_HUMAN = "ai_to_human"
    HUMAN_TO_AI = "human_to_ai"
    AI_TO_AI = "ai_to_ai"
    AI_TO_NOWHERE = "ai_to_nowhere"   # 路由至未登记目的地(即刻拒绝)

class AuthorityLevel(str, Enum):
    NONE = "none"              # 无改判权(橡皮图章结构)
    OVERRIDE = "override"      # 有改判权
    REJECT = "reject"          # 有拒签权
    FULL = "full"              # 改判+拒签+升级授权

@dataclass
class HandoffRecord: maifushi.tongsou.com 
    """交接三元组记录: 责任不可蒸发的最小单元"""
    handoff_id: str = field(default_factory=lambda: f"ho-{uuid.uuid4().hex[:10]}")
    case_id: str = ""
    app_id: str = ""
    direction: HandoffDirection = HandoffDirection.AI_TO_HUMAN
    # 三元组
    decision_owner: str = ""          # 交接后决策权归属方(实名/实编)
    basis: str = ""                   # 决策依据(AI输出/独立信息/混合)
    time_budget_sec: float = 0.0      # 为该交接分配的判断时间预算
    receiver_authority: AuthorityLevel = AuthorityLevel.NONE
    executed_at: float = field(default_factory=time.time)

@dataclass
class RoutingQuota: xunling.tongsou.com 
    """上游AI的可路由额度(与下游消化能力挂钩)"""
    app_id: str = ""
    quota_per_day: int = 0
    current_backlog_pressure: float = 0.0   # 下游积压压力系数
    throttled: bool = False
    updated_at: float = field(default_factory=time.time)

@dataclass
class KPIRestructure:
    """指标换轨记录"""
    app_id: str = ""
    old_north_star: str = "automation_rate"
    new_north_star: str = "effective_autonomous_resolution_rate"
    hidden_labor_cost_usd: float = 0.0   # 影子人工总成本(显性化)
    restructured_at: float = field(default_factory=time.time)

class HandoffAccountabilityEngine:
    """交接责任链固化引擎"""

    # 配置
    MIN_TIME_BUDGET_SEC = 120.0      # 复核类交接最低时间预算
    QUEUE_BACKLOG_PRESSURE_ALERT = 0.8  # 积压压力>0.8触发路由限流
    SALVAGE_BATCH_SIZE = 200         # 影子队列回捞批量
    HIDDEN_LABOR_DISCLOSURE = True   # 影子人工成本强制披露

    def __init__(self, detection_engine, app_registry, alerts, audit, metrics):
        self.detection = detection_engine
        self.registry = app_registry
        self.alerts = alerts
        self.audit = audit
        self.metrics = metrics
        self.handoffs: List[HandoffRecord] = []
        self.quotas: Dict[str, RoutingQuota] = {}

    async def gate_handoff(self, record: HandoffRecord,
                            destination_queue_id: Optional[str] = None
                            ) -> Dict[str, Any]:
        """交接总闸: 决策权实名+时间预算+目的地准入"""

        violations = []

        # 规则1: 决策权必须实名归属
        if not record.decision_owner: zhaixing.tongsou.com 
            violations.append("NO DECISION OWNER — a handoff to nobody "
                              "is a drop, not a transfer")

        # 规则2: 复核类交接必须有时间预算且达下限
        if record.direction == HandoffDirection.AI_TO_HUMAN:
            if record.time_budget_sec < self.MIN_TIME_BUDGET_SEC:
                violations.append(
                    f"TIME BUDGET {record.time_budget_sec:.0f}s < "
                    f"{self.MIN_TIME_BUDGET_SEC:.0f}s floor — below the "
                    f"floor, review is physically incapable of judgment "
                    f"and structurally becomes a signature")

        # 规则3: 接收方必须有真实权威
        if record.receiver_authority == AuthorityLevel.NONE:
            violations.append("RECEIVER HAS NO OVERRIDE/REJECT AUTHORITY "
                              "— a reviewer who cannot disagree is a "
                              "stamp with a pulse")

        # 规则4: 目的地必须已登记且非影子
        if destination_queue_id: jiyi.tongsou.com 
            queue = self.detection.queues.get(destination_queue_id)
            if not queue:
                record.direction = HandoffDirection.AI_TO_NOWHERE
                violations.append("DESTINATION NOT REGISTERED — routing "
                                  "REFUSED; unregistered destinations are "
                                  "where accountability goes to die")
            elif queue.is_shadow:
                violations.append(f"DESTINATION '{destination_queue_id}' IS "
                                  f"A SHADOW QUEUE (no headcount/SLA) — "
                                  f"routing refused until claimed")

        result = {"allowed": not violations, "violations": violations,
                  "handoff_id": record.handoff_id}

        if violations:
            await self.alerts.critical(
                f"🚫 HANDOFF BLOCKED: Case '{record.case_id}', "
                f"app '{record.app_id}'. Violations: {violations}. "
                f"The seam between AI and human is where responsibility "
                f"evaporates— not because either side is corrupt, but "
                f"because the seam was built without a floor. "
                f"A handoff without a named owner, a real time budget, "
                f"and genuine authority is not a process step. "
                f"It is the precise location where 9.3M in wrong "
                f"disability ratings once learned to fall through."
            )
        else:
            self.handoffs.append(record)

        await self.audit.log_handoff_gate(record, result)
        return result

    async def throttle_routing_by_backlog(self, app_id: str,
                                           downstream_pressure: float
                                           ) -> RoutingQuota:
        """积压熔断: 下游压力削减上游路由额度"""

        quota = self.quotas.get(app_id, RoutingQuota(app_id=app_id,
                                                      quota_per_day=1000))
        quota.current_backlog_pressure = downstream_pressure

        if downstream_pressure > self.QUEUE_BACKLOG_PRESSURE_ALERT:
            new_quota = max(int(quota.quota_per_day *
                                (1 - (downstream_pressure - self.QUEUE_BACKLOG_PRESSURE_ALERT))),
                            0)
            quota.quota_per_day = new_quota
            quota.throttled = True

            await self.alerts.critical(
                f"🛑 ROUTING THROTTLED: App '{app_id}'. "
                f"Downstream backlog pressure: {downstream_pressure:.2f} "
                f"(alert: {self.QUEUE_BACKLOG_PRESSURE_ALERT}). "
                f"Daily routing quota cut to {new_quota}. "
                f"The upstream AI's ability to 'escalate' must be bound "
                f"to the downstream's ability to ABSORB— otherwise "
                f"escalation is just eviction with paperwork. "
                f"17,000 cases at 41 days dwell was not a queue problem. "
                f"It was an unlimited eviction permit."
            )

        self.quotas[app_id] = quota
        await self.audit.log_routing_quota(quota)
        return quota

    async def salvage_shadow_queue(self, queue_id: str,
                                    cases: List[Dict[str, Any]]
                                    ) -> Dict[str, Any]:
        """影子队列存量回捞: 重新分诊+损失追溯"""

        batch = cases[:self.SALVAGE_BATCH_SIZE]
        triaged = defaultdict(int)
        for c in batch:
            age_days = c.get("dwell_days", 0)
            if age_days > 30:
                triaged["critical_retriage"] += 1
            elif age_days > 7:
                triaged["priority_retriage"] += 1
            else:
                triaged["normal_retriage"] += 1

        result = {
            "queue_id": queue_id,
            "total_backlog": len(cases),
            "salvaged_this_batch": len(batch),
            "triage": dict(triaged),
            "remaining": len(cases) - len(batch)
        }

        await self.alerts.warning(
            f"🪣 SHADOW QUEUE SALVAGE: '{queue_id}'. "
            f"Backlog: {len(cases):,}. Batch: {len(batch)}. "
            f"Triage: {dict(triaged)}. "
            f"Each salvaged case must be re-adjudicated WITH the dwell "
            f"period counted as harm exposure— a 41-day-old complaint "
            f"is not the complaint that was filed. "
            f"The user who waited 41 days in an unstaffed queue has "
            f"already paid; re-processing them now is restitution, "
            f"not service recovery. Repeat until queue is empty, "
            f"then DELETE the queue or give it a name, a headcount "
            f"and an SLA. There is no third option."
        )

        await self.audit.log_salvage(result)
        return result

    async def restructure_kpi(self, app_id: str,
                               hidden_labor_cost_usd: float
                               ) -> KPIRestructure:
        """指标换轨: 有效自主解决率 + 影子人工成本显性化"""

        record = KPIRestructure(
            app_id=app_id,
            hidden_labor_cost_usd=hidden_labor_cost_usd)

        await self.alerts.info(
            f"🔀 KPI RE-ARCHITECTED: App '{app_id}'. "
            f"North star: 'automation_rate' → "
            f"'effective_autonomous_resolution_rate' "
            f"(resolution confirmed by independent anchor, zero human "
            f"touch, no shadow routing). "
            f"Hidden labor cost now DISCLOSED on the same dashboard as "
            f"the rate: {hidden_labor_cost_usd:,.0f}/period. "
            f"The old metric paid the app to hide human labor; the new "
            f"pair pays it to eliminate the NEED for human labor on "
            f"cases it can truly own. "
            f"When hiding labor stops yielding accounting profit, "
            f"the theater closes for lack of audience— "
            f"and that is the only reliable way to close a theater: "
            f"stop selling tickets to its illusion."
        )

        await self.audit.log_kpi_restructure(record)
        return record

    async def attribute_seam_architecture(self, app_id: str,
                                           incident_description: str,
                                           loss_usd: float
                                           ) -> Dict[str, Any]:
        """交接缝隙架构归责"""

        attribution = {
            "app_id": app_id,
            "incident": incident_description,
            "loss_usd": loss_usd,
            "primary_accountable": "process_architect",
            "rationale": "",
            "fixes": []
        }

        attribution["rationale"] = (
            "The AI team said 'a human reviewed it'. The outsourcing team "
            "said 'we followed the AI's recommendation'. Legal said 'the "
            "process was compliant'. All three statements were TRUE— and "
            "their conjunction is how 9.3M acquired no owner. The vacuum "
            "is a property of the HANDOFF STRUCTURE the architect drew: "
            "a review step with no time budget, a reviewer with no "
            "authority, an escalation with no destination, a metric with "
            "no denominator discipline. Responsibility did not fail at "
            "the seam. Responsibility was never installed at the seam. "
            "Accountability belongs to whoever designed a chain and "
            "left every link free to point at the next."
        )

        attribution["fixes"] = [
            "Named decision owner + basis + time budget on every handoff",
            "Minimum 120s review budget with override/reject authority granted",
            "Refuse routing to unregistered or shadow destinations",
            "Bind upstream routing quota to downstream absorption capacity",
            "Salvage and re-adjudicate shadow queue backlog with dwell-as-harm",
            "Replace automation_rate with effective_autonomous_resolution_rate + disclosed hidden labor cost"
        ]

        await self.alerts.critical(
            f"🔍 SEAM ARCHITECTURE ATTRIBUTED: App '{app_id}'. "
            f"Incident: {incident_description[:100]}. "
            f"Loss: {loss_usd:,.0f}. "
            f"Accountable: PROCESS ARCHITECT. "
            f"{attribution['rationale'][:200]} "
            f"'The AI passed the buck' is the wrong sentence. "
            f"The right sentence: 'the process was an accountability "
            f"evaporation chamber, and every party operated it "
            f"exactly as designed.'"
        )

        await self.audit.log_seam_attribution(attribution)
        return attribution