diff --git a/backend/agenteval/intelligent_eval/decision_logs.py b/backend/agenteval/intelligent_eval/decision_logs.py
index 2d40b79..824b05b 100644
--- a/backend/agenteval/intelligent_eval/decision_logs.py
+++ b/backend/agenteval/intelligent_eval/decision_logs.py
@@ -7,7 +7,12 @@ from typing import Any
 
 from sqlmodel import Session, select
 
-from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalDecisionLogDB
+from agenteval.intelligent_eval.models import IntelligentEvalStatus
+from agenteval.storage.db import (
+    IntelligentEvalDB,
+    IntelligentEvalDecisionLogDB,
+    IntelligentEvalSessionDB,
+)
 
 
 def _log_to_dict(log: IntelligentEvalDecisionLogDB) -> dict:
@@ -22,7 +27,12 @@ def _log_to_dict(log: IntelligentEvalDecisionLogDB) -> dict:
     }
 
 
-def create_decision_log(
+def _require_eval(eval_id: str, session: Session) -> None:
+    if session.get(IntelligentEvalDB, eval_id) is None:
+        raise LookupError(f"intelligent eval {eval_id} not found")
+
+
+def _append_row(
     eval_id: str,
     decision_type: str,
     reason: str,
@@ -30,30 +40,6 @@ def create_decision_log(
     context: dict[str, Any],
     session: Session,
 ) -> dict:
-    """Create a decision log entry, or return the existing one if the
-    (eval_id, decision_type, context) tuple is already recorded.
-
-    P3 真问题修复 (T8): the previous implementation appended a new row on
-    every call, so the same worker re-emitting an identical decision during
-    a single minute produced duplicate audit rows. Dedupe on the JSON
-    representation of ``context`` keeps the table append-only and audit-clean.
-
-    Raises ``LookupError`` if eval not found.
-    """
-    eval_db = session.get(IntelligentEvalDB, eval_id)
-    if eval_db is None:
-        raise LookupError(f"intelligent eval {eval_id} not found")
-
-    # Dedupe: same (eval, decision_type, context) → return existing.
-    for existing in session.exec(
-        select(IntelligentEvalDecisionLogDB).where(
-            IntelligentEvalDecisionLogDB.eval_id == eval_id,
-            IntelligentEvalDecisionLogDB.decision_type == decision_type,
-        )
-    ).all():
-        if existing.get_context() == context:
-            return _log_to_dict(existing)
-
     log = IntelligentEvalDecisionLogDB(
         eval_id=eval_id,
         decision_type=decision_type,
@@ -67,6 +53,69 @@ def create_decision_log(
     return _log_to_dict(log)
 
 
+def create_decision_log(
+    eval_id: str,
+    decision_type: str,
+    reason: str,
+    cron_id: str,
+    context: dict[str, Any],
+    session: Session,
+) -> dict:
+    """Agent-reporting entry: create a decision log, or return the existing
+    one if the (eval_id, decision_type, context) tuple is already recorded.
+
+    P3 真问题修复 (T8 / Gitea #6): agent 会在同一分钟内重复上报相同决策，
+    按 context JSON 去重保表干净。**去重只服务 agent 上报路径**——平台落账
+    每次都是新事实，用 ``append_decision_log``，调用方无需知道去重存在。
+
+    Raises ``LookupError`` if eval not found.
+    """
+    _require_eval(eval_id, session)
+
+    # Dedupe: same (eval, decision_type, context) → return existing.
+    for existing in session.exec(
+        select(IntelligentEvalDecisionLogDB).where(
+            IntelligentEvalDecisionLogDB.eval_id == eval_id,
+            IntelligentEvalDecisionLogDB.decision_type == decision_type,
+        )
+    ).all():
+        if existing.get_context() == context:
+            return _log_to_dict(existing)
+
+    return _append_row(eval_id, decision_type, reason, cron_id, context, session)
+
+
+def append_decision_log(
+    eval_id: str,
+    decision_type: str,
+    reason: str,
+    cron_id: str,
+    context: dict[str, Any],
+    session: Session,
+) -> dict:
+    """Platform-bookkeeping entry: 纯追加，不去重。
+
+    平台每次落账都是新事实（attempt 递增、task_id 不同、闸门判定），
+    相同 context 也总是追加一行。
+
+    Raises ``LookupError`` if eval not found.
+    """
+    _require_eval(eval_id, session)
+    return _append_row(eval_id, decision_type, reason, cron_id, context, session)
+
+
+def count_decisions(eval_id: str, decision_type: str, session: Session) -> int:
+    """该评估某类型决策日志的条数（平台闸门计数与 attempt 落账的计数原语）。"""
+    return len(
+        session.exec(
+            select(IntelligentEvalDecisionLogDB).where(
+                IntelligentEvalDecisionLogDB.eval_id == eval_id,
+                IntelligentEvalDecisionLogDB.decision_type == decision_type,
+            )
+        ).all()
+    )
+
+
 def list_decision_logs(eval_id: str, session: Session) -> list[dict]:
     """List decision logs for an eval. Raises ``LookupError`` if eval not found."""
     eval_db = session.get(IntelligentEvalDB, eval_id)
@@ -80,3 +129,108 @@ def list_decision_logs(eval_id: str, session: Session) -> list[dict]:
     ).all()
 
     return [_log_to_dict(log) for log in logs]
+
+
+def supplement_decision_logs(session: Session) -> int:
+    """Platform audit backfill for decision logs.
+
+    方案③的决策日志由 OpenClaw agent 上报（LLM 自主，尽力而为）——异常路径
+    （如卡死恢复后重试）agent 可能跳过上报，导致决策过程页面为空。这里按评估
+    状态推导决策并补录：
+
+    - EXECUTING：欠账（completed < estimated）补 execute_session，所有会话
+      完成后补 start_analysis。
+    - COMPLETED：历史评估/异常路径可能完全没有决策日志，回填 execute_session
+      （按 plan 时段逐条）+ start_analysis，让旧报告也有决策过程可看。
+
+    只补"该类型缺失"的，不重复；且只记录状态，不改变 agent 的实际执行。
+
+    Returns:
+        补录的决策日志条数。
+    """
+    evals = session.exec(
+        select(IntelligentEvalDB).where(
+            IntelligentEvalDB.status.in_(
+                [
+                    IntelligentEvalStatus.EXECUTING.value,
+                    IntelligentEvalStatus.COMPLETED.value,
+                ]
+            )
+        )
+    ).all()
+
+    added = 0
+    for ev in evals:
+        plan = ev.get_plan() if ev.plan else {}
+        estimated = plan.get("estimated_sessions", 0)
+        sessions = session.exec(select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)).all()
+        completed = sum(1 for s in sessions if s.status == "completed")
+        types = {
+            x.decision_type
+            for x in session.exec(
+                select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
+            ).all()
+        }
+        if ev.status == IntelligentEvalStatus.EXECUTING.value:
+            if "execute_session" not in types and completed < estimated:
+                _append_row(
+                    ev.id,
+                    "execute_session",
+                    "平台兜底：时段欠账需执行会话",
+                    "platform",
+                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                    session,
+                )
+                added += 1
+            elif "start_analysis" not in types and sessions and completed >= estimated:
+                _append_row(
+                    ev.id,
+                    "start_analysis",
+                    "平台兜底：所有会话已完成开始分析",
+                    "platform",
+                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                    session,
+                )
+                added += 1
+        elif ev.status == IntelligentEvalStatus.COMPLETED.value:
+            # 历史回填：completed 评估决策日志全缺失时，按时段补 execute_session
+            if "execute_session" not in types:
+                slots = plan.get("time_distribution") or []
+                if slots:
+                    for slot in slots:
+                        _append_row(
+                            ev.id,
+                            "execute_session",
+                            f"平台兜底：时段{slot.get('time_slot', '')}执行会话（历史回填）",
+                            "platform",
+                            {
+                                "platform_supplemented": True,
+                                "time_slot": slot.get("time_slot"),
+                                "sessions": slot.get("sessions"),
+                                "completed": completed,
+                                "estimated": estimated,
+                            },
+                            session,
+                        )
+                        added += 1
+                else:
+                    _append_row(
+                        ev.id,
+                        "execute_session",
+                        "平台兜底：执行会话（历史回填）",
+                        "platform",
+                        {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                        session,
+                    )
+                    added += 1
+            if "start_analysis" not in types and sessions:
+                _append_row(
+                    ev.id,
+                    "start_analysis",
+                    "平台兜底：所有会话已完成开始分析（历史回填）",
+                    "platform",
+                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
+                    session,
+                )
+                added += 1
+    return added
diff --git a/backend/agenteval/intelligent_eval/lifecycle.py b/backend/agenteval/intelligent_eval/lifecycle.py
index 5339582..338f901 100644
--- a/backend/agenteval/intelligent_eval/lifecycle.py
+++ b/backend/agenteval/intelligent_eval/lifecycle.py
@@ -412,7 +412,7 @@ def expire_stale_running_sessions(session: Session) -> int:
     from sqlalchemy import func
     from sqlmodel import select
 
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
+    from agenteval.intelligent_eval.decision_logs import append_decision_log
     from agenteval.storage.db import IntelligentEvalMessageDB, IntelligentEvalSessionDB
 
     now = utc_now()
@@ -435,7 +435,7 @@ def expire_stale_running_sessions(session: Session) -> int:
         row.status = IntelligentEvalSessionStatus.EXPIRED.value
         row.closed_at = now
         expired += 1
-        create_decision_log(
+        append_decision_log(
             row.eval_id,
             "session_expired",
             f"平台兜底：会话 {SESSION_IDLE_EXPIRE_MINUTES} 分钟无新轮次，置为过期（不完整证据）",
@@ -486,7 +486,6 @@ def record_planner_triggers(session: Session) -> int:
 
     触发次数即日志条数（含触发失败——attempt 在触发动作发生前落账），供
     enforce_planning_gates 计闸，也让决策过程页可见平台尝试过多少次。
-    context 带递增 attempt 序号避免 create_decision_log 去重合并。
     ADR-0011：同一评估 10min 冷却内的扫描节拍不重复触发（孤儿 agent 双管），
     冷却中的评估不计次数。
 
@@ -495,8 +494,8 @@ def record_planner_triggers(session: Session) -> int:
     """
     from sqlmodel import select
 
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
-    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalDecisionLogDB
+    from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
+    from agenteval.storage.db import IntelligentEvalDB
 
     now = utc_now().replace(tzinfo=None)
     planning = session.exec(
@@ -506,18 +505,8 @@ def record_planner_triggers(session: Session) -> int:
     for row in planning:
         if _in_trigger_cooldown(session, row.id, "planner_trigger", now):
             continue
-        attempt = (
-            len(
-                session.exec(
-                    select(IntelligentEvalDecisionLogDB).where(
-                        IntelligentEvalDecisionLogDB.eval_id == row.id,
-                        IntelligentEvalDecisionLogDB.decision_type == "planner_trigger",
-                    )
-                ).all()
-            )
-            + 1
-        )
-        create_decision_log(
+        attempt = count_decisions(row.id, "planner_trigger", session) + 1
+        append_decision_log(
             row.id,
             "planner_trigger",
             f"平台第 {attempt} 次触发 planner 生成粗计划",
@@ -541,25 +530,11 @@ def worker_trigger_candidates(session: Session) -> list[str]:
 
 def record_worker_triggers(session: Session, eval_ids: list[str]) -> None:
     """为本次 worker 触发覆盖的评估落账 worker_trigger（供冷却与可见性）。"""
-    from sqlmodel import select
-
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
-    from agenteval.storage.db import IntelligentEvalDecisionLogDB
+    from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
 
     for eval_id in eval_ids:
-        # attempt 递增保证 context 唯一——否则 create_decision_log 去重后冷却时间不前进
-        attempt = (
-            len(
-                session.exec(
-                    select(IntelligentEvalDecisionLogDB).where(
-                        IntelligentEvalDecisionLogDB.eval_id == eval_id,
-                        IntelligentEvalDecisionLogDB.decision_type == "worker_trigger",
-                    )
-                ).all()
-            )
-            + 1
-        )
-        create_decision_log(
+        attempt = count_decisions(eval_id, "worker_trigger", session) + 1
+        append_decision_log(
             eval_id,
             "worker_trigger",
             f"平台第 {attempt} 次触发 worker 执行会话/分析",
@@ -583,8 +558,8 @@ def enforce_planning_gates(session: Session) -> int:
     """
     from sqlmodel import select
 
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
-    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalDecisionLogDB
+    from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
+    from agenteval.storage.db import IntelligentEvalDB
 
     now = utc_now().replace(tzinfo=None)
     planning = session.exec(
@@ -593,14 +568,7 @@ def enforce_planning_gates(session: Session) -> int:
 
     failed = 0
     for row in planning:
-        attempts = len(
-            session.exec(
-                select(IntelligentEvalDecisionLogDB).where(
-                    IntelligentEvalDecisionLogDB.eval_id == row.id,
-                    IntelligentEvalDecisionLogDB.decision_type == "planner_trigger",
-                )
-            ).all()
-        )
+        attempts = count_decisions(row.id, "planner_trigger", session)
         entered_at = row.updated_at or row.created_at
         age_minutes = (now - entered_at).total_seconds() / 60 if entered_at else 0
         if attempts >= PLANNING_MAX_ATTEMPTS:
@@ -614,7 +582,7 @@ def enforce_planning_gates(session: Session) -> int:
         row.updated_at = utc_now()
         row.completed_at = utc_now()
         failed += 1
-        create_decision_log(
+        append_decision_log(
             row.id,
             "planning_gate_failed",
             f"平台兜底：{reason}",
@@ -651,7 +619,7 @@ def enforce_executing_ceiling(session: Session) -> int:
     """
     from sqlmodel import select
 
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
+    from agenteval.intelligent_eval.decision_logs import append_decision_log
     from agenteval.storage.db import IntelligentEvalDB
 
     now = utc_now().replace(tzinfo=None)
@@ -677,7 +645,7 @@ def enforce_executing_ceiling(session: Session) -> int:
         row.updated_at = utc_now()
         row.completed_at = utc_now()
         failed += 1
-        create_decision_log(
+        append_decision_log(
             row.id,
             "executing_timeout",
             f"平台兜底：{reason}",
@@ -725,11 +693,8 @@ def evals_needing_analyst_nudge(session: Session) -> list[str]:
     """
     from sqlmodel import select
 
-    from agenteval.storage.db import (
-        IntelligentEvalDB,
-        IntelligentEvalDecisionLogDB,
-        IntelligentEvalSessionDB,
-    )
+    from agenteval.intelligent_eval.decision_logs import count_decisions
+    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB
 
     now = utc_now().replace(tzinfo=None)
     executing = session.exec(
@@ -753,13 +718,7 @@ def evals_needing_analyst_nudge(session: Session) -> list[str]:
         last_closed = max(closed_moments)
         if (now - last_closed).total_seconds() < ANALYST_NUDGE_DELAY_MINUTES * 60:
             continue
-        nudges = session.exec(
-            select(IntelligentEvalDecisionLogDB).where(
-                IntelligentEvalDecisionLogDB.eval_id == row.id,
-                IntelligentEvalDecisionLogDB.decision_type == "analyst_nudge",
-            )
-        ).all()
-        if len(nudges) >= ANALYST_NUDGE_MAX:
+        if count_decisions(row.id, "analyst_nudge", session) >= ANALYST_NUDGE_MAX:
             continue
         # 冷却复用（ADR-0011）：最近的 analyst_nudge 或 worker_trigger 都会
         # 唤起 agent（最长 10min），冷却期内不再催促以免堆叠孤儿 agent
@@ -776,23 +735,10 @@ def evals_needing_analyst_nudge(session: Session) -> list[str]:
 
 def record_analyst_nudge(session: Session, eval_id: str) -> None:
     """落账一次 analyst 催促（analyst_nudge 决策日志，attempt 递增供上限计数）。"""
-    from sqlmodel import select
+    from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
 
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
-    from agenteval.storage.db import IntelligentEvalDecisionLogDB
-
-    attempt = (
-        len(
-            session.exec(
-                select(IntelligentEvalDecisionLogDB).where(
-                    IntelligentEvalDecisionLogDB.eval_id == eval_id,
-                    IntelligentEvalDecisionLogDB.decision_type == "analyst_nudge",
-                )
-            ).all()
-        )
-        + 1
-    )
-    create_decision_log(
+    attempt = count_decisions(eval_id, "analyst_nudge", session) + 1
+    append_decision_log(
         eval_id,
         "analyst_nudge",
         f"平台第 {attempt} 次催促 analyst 汇总报告（所有会话已终态）",
@@ -824,31 +770,17 @@ def eval_ids_with_pending_worker_tasks(session: Session) -> list[str]:
 def record_trigger_failures(session: Session, *, channel: str, eval_ids: list[str], error: str) -> int:
     """触发失败落账：为每个受影响评估写一条 trigger_failed 决策日志。
 
-    channel 区分 "worker" / "planner"；context 带递增序号防 create_decision_log
-    去重合并。失败原因截断落 reason，供决策过程页可见。
+    channel 区分 "worker" / "planner"；失败原因截断落 reason，供决策过程页可见。
 
     Returns:
         落账条数。
     """
-    from sqlmodel import select
-
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
-    from agenteval.storage.db import IntelligentEvalDecisionLogDB
+    from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
 
     recorded = 0
     for eval_id in eval_ids:
-        attempt = (
-            len(
-                session.exec(
-                    select(IntelligentEvalDecisionLogDB).where(
-                        IntelligentEvalDecisionLogDB.eval_id == eval_id,
-                        IntelligentEvalDecisionLogDB.decision_type == "trigger_failed",
-                    )
-                ).all()
-            )
-            + 1
-        )
-        create_decision_log(
+        attempt = count_decisions(eval_id, "trigger_failed", session) + 1
+        append_decision_log(
             eval_id,
             "trigger_failed",
             f"平台触发 {channel} 失败（第 {attempt} 次）：{error[:200]}",
@@ -861,13 +793,13 @@ def record_trigger_failures(session: Session, *, channel: str, eval_ids: list[st
 
 
 def _fail_eval(session: Session, row, reason: str, decision_type: str, context: dict[str, Any]) -> None:
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
+    from agenteval.intelligent_eval.decision_logs import append_decision_log
 
     row.status = IntelligentEvalStatus.FAILED.value
     row.plan_feedback = reason
     row.updated_at = utc_now()
     row.completed_at = utc_now()
-    create_decision_log(
+    append_decision_log(
         row.id,
         decision_type,
         f"平台兜底：{reason}",
diff --git a/backend/agenteval/intelligent_eval/scheduler.py b/backend/agenteval/intelligent_eval/scheduler.py
new file mode 100644
index 0000000..8dd39ad
--- /dev/null
+++ b/backend/agenteval/intelligent_eval/scheduler.py
@@ -0,0 +1,311 @@
+"""Intelligent-eval scheduler runtime（触发式执行的平台节奏）。
+
+从 web/app.py 抽出（ADR-0009 / ADR-0011）：平台掌控节奏——每 60s 扫描
+executing 评估，watchdog 兜底、任务入队、决策日志补录，然后按需触发
+无状态 OpenClaw agent（planner/worker）执行。web 层只负责 start/stop
+本 runtime，不再知道扫描循环的细节。
+
+对外 interface 只有三个入口：``scheduler_runtime.start()`` /
+``.stop()`` / ``scan_once()``（单次扫描节拍，即测试面）。
+"""
+
+import asyncio
+import logging
+import subprocess
+import time
+from typing import Callable, Optional
+
+from agenteval.storage.db import get_session
+
+_logger = logging.getLogger("agenteval")
+
+SCAN_INTERVAL_SECONDS = 60
+
+# 触发指令必须带"立即执行"语义：`openclaw agent` 无 cron state，若只发
+# skill 名，agent 会按 skill 的"跨节拍"设计决策后等下一拍而死锁。
+WORKER_MSG = (
+    "执行 agenteval-intelligent-worker skill，立即完成当前任务，不要等待下一节拍："
+    "仅执行当前时间对应时段（time_distribution 中当前 offset 所在时段）内欠账的会话"
+    "——按评估 started_at 与当前时间精确判断当前时段，只创建该时段计划内的会话，"
+    "绝不创建未来时段的会话，未来时段到期后平台会再次触发你；"
+    "仅当所有时段计划内的会话都已达到终态（completed/failed/expired）时，"
+    "才调用 agenteval-intelligent-analyst skill 完成分析并生成报告；"
+    "若还有未到期的未来时段，保持等待、本次不做任何操作。"
+)
+
+PLANNER_MSG = (
+    "执行 agenteval-intelligent-planner skill，立即完成当前任务，不要等待下一节拍："
+    "为 planning 状态的智能评估读取输入、产出粗计划并提交平台审批。"
+)
+
+
+async def _trigger_openclaw_agent(
+    role: str,
+    session_prefix: str,
+    message: str,
+    on_failure: Callable[[str], None],
+) -> None:
+    """触发原语：docker exec 唤醒一个无状态 headless OpenClaw agent。
+
+    方案③（避免外部 IM channel）：`--deliver` 默认 false，不经 cron
+    delivery channel。ADR-0011 孤儿 agent 双管之一：容器内命令包
+    `timeout 600`——agent 进程最多跑 10 分钟即被强杀。
+
+    必须用独立 session：`--agent main` 复用 main 持久 session，多次触发
+    累积上下文缓存（~12 万 token）后 LLM 不再执行 skill 的 API 步骤，
+    直接幻觉输出而不取任务。
+
+    失败（子进程缺失/超时/非零退出）调用 ``on_failure(error)`` 落账。
+    """
+    cmd = [
+        "docker",
+        "exec",
+        "openclaw-eval",
+        # ADR-0011：容器内 timeout 强杀，agent 最长存活 10min
+        "timeout",
+        "600",
+        "openclaw",
+        "agent",
+        "--agent",
+        "main",
+        "--session-id",
+        f"{session_prefix}-{int(time.time())}",
+        "-m",
+        message,
+        "--json",
+    ]
+    try:
+        proc = await asyncio.to_thread(
+            subprocess.run,
+            cmd,
+            capture_output=True,
+            text=True,
+            timeout=660,  # 容器内 timeout 600 + 启动/回收余量
+        )
+        _logger.info("%s 触发完成 exit=%s", role, proc.returncode)
+        if proc.returncode != 0:
+            _logger.warning("%s 触发 stderr: %s", role, proc.stderr[-300:])
+            on_failure(f"exit={proc.returncode} stderr={proc.stderr[-200:]}")
+    except Exception as exc:
+        _logger.warning("%s 触发失败（忽略）: %s", role, exc)
+        on_failure(str(exc))
+
+
+def _record_worker_trigger_failure(error: str) -> None:
+    """ADR-0011：worker 触发失败落账到受影响评估（有待认领任务的评估）。"""
+    from agenteval.intelligent_eval.lifecycle import (
+        eval_ids_with_pending_worker_tasks,
+        record_trigger_failures,
+    )
+
+    session = get_session()
+    try:
+        eval_ids = eval_ids_with_pending_worker_tasks(session)
+        if eval_ids:
+            record_trigger_failures(session, channel="worker", eval_ids=eval_ids, error=error)
+    except Exception as exc:
+        _logger.warning("触发失败落账失败（忽略）: %s", exc)
+    finally:
+        session.close()
+
+
+def _record_planner_trigger_failure(error: str) -> None:
+    """ADR-0011：planner 触发失败落账到所有 planning 评估。"""
+    from sqlmodel import select
+
+    from agenteval.intelligent_eval.lifecycle import record_trigger_failures
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB
+
+    session = get_session()
+    try:
+        eval_ids = [
+            row.id
+            for row in session.exec(
+                select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.PLANNING.value)
+            ).all()
+        ]
+        if eval_ids:
+            record_trigger_failures(session, channel="planner", eval_ids=eval_ids, error=error)
+    except Exception as exc:
+        _logger.warning("触发失败落账失败（忽略）: %s", exc)
+    finally:
+        session.close()
+
+
+async def trigger_worker(nudge_eval_ids: Optional[list[str]] = None) -> bool:
+    """触发 OpenClaw headless agent 执行 worker skill。
+
+    平台只当"触发闹钟"——有冷却期外的候选评估时，用 docker exec 唤醒
+    agent，worker skill 由 agent 自主执行（决策/建会话/真实对话/上报）。
+    per-eval 触发冷却 10min（worker_trigger 决策日志记录）——agent 还在跑
+    的评估不重复触发，避免 60s 扫描节奏堆叠并发 agent。
+
+    nudge_eval_ids：analyst 兜底催促的评估（所有会话终态、无任务队列条目），
+    与候选任务评估合并去重后一同落账触发。
+
+    Returns:
+        True 若确实触发了 agent（存在冷却期外的服务对象）。
+    """
+    from agenteval.intelligent_eval.lifecycle import (
+        record_worker_triggers,
+        worker_trigger_candidates,
+    )
+
+    session = get_session()
+    try:
+        trigger_ids = sorted(set(worker_trigger_candidates(session)) | set(nudge_eval_ids or []))
+        if not trigger_ids:
+            return False
+        # 触发前落账（含后续失败也计入冷却），防止 agent 在跑期间重复触发
+        record_worker_triggers(session, trigger_ids)
+    finally:
+        session.close()
+
+    await _trigger_openclaw_agent("Worker", "agenteval-worker", WORKER_MSG, _record_worker_trigger_failure)
+    return True
+
+
+async def trigger_planner() -> bool:
+    """触发 OpenClaw headless agent 执行 planner skill。
+
+    方案③只自动化了 executing（worker）→ completed（analyst），planning
+    阶段由本函数补上：对 planning 状态评估触发 planner skill，planner 自会
+    拉取 planning 评估列表、读取四件套、产出粗计划并 PUT /plan 提交。
+    处理完评估离开 planning 后不再触发。
+
+    Returns:
+        True 若确实触发了 agent（存在 planning 评估）。
+    """
+    # ADR-0011：触发前先落账 planner_trigger（含后续失败也计入双闸），
+    # 0 个 planning 评估时不触发。
+    from agenteval.intelligent_eval.lifecycle import record_planner_triggers
+
+    session = get_session()
+    try:
+        planning_count = record_planner_triggers(session)
+    finally:
+        session.close()
+    if planning_count == 0:
+        return False
+
+    await _trigger_openclaw_agent("Planner", "agenteval-planner", PLANNER_MSG, _record_planner_trigger_failure)
+    return True
+
+
+def _fire_and_forget(coro, name: str) -> None:
+    """ADR-0011：触发改为 fire-and-forget——agent 最长跑 10min，await 会把
+    60s 扫描节奏拖到 10min+。派生 asyncio task，异常在完成回调中记录
+    （触发失败已由触发函数自身落账 trigger_failed）。"""
+
+    def _done(task: asyncio.Task) -> None:
+        if task.cancelled():
+            return
+        exc = task.exception()
+        if exc:
+            _logger.warning("%s 触发任务异常（忽略）: %s", name, exc)
+
+    asyncio.create_task(coro).add_done_callback(_done)
+
+
+def scan_once() -> None:
+    """单次扫描节拍：watchdog 兜底 → 入队 → 任务回收 → 决策日志补录 →
+    analyst 催促 → fire-and-forget 触发 worker/planner。失败不阻断
+    （下次节拍继续）。"""
+    try:
+        session = get_session()
+        try:
+            from agenteval.intelligent_eval.decision_logs import supplement_decision_logs
+            from agenteval.intelligent_eval.lifecycle import (
+                enforce_executing_ceiling,
+                enforce_planning_gates,
+                enforce_trigger_failure_gates,
+                expire_stale_running_sessions,
+            )
+            from agenteval.intelligent_eval.task_queue import (
+                requeue_stale_assigned_tasks,
+                scan_and_enqueue_tasks,
+                settle_tasks_for_finished_evals,
+            )
+
+            r = requeue_stale_assigned_tasks(session)
+            if r:
+                _logger.info("卡死恢复：%d 个 assigned 任务重新入队", r)
+            x = expire_stale_running_sessions(session)
+            if x:
+                _logger.info("会话过期：%d 个 running 会话 60 分钟无新轮次，置为 expired", x)
+            g = enforce_planning_gates(session)
+            if g:
+                _logger.info("planning 双闸：%d 个评估超限判失败", g)
+            c = enforce_executing_ceiling(session)
+            if c:
+                _logger.info("executing 兜底：%d 个评估超窗判失败", c)
+            f = enforce_trigger_failure_gates(session)
+            if f:
+                _logger.info("触发失败判死：%d 个评估连续触发失败超限判失败", f)
+            n = scan_and_enqueue_tasks(session)
+            if n:
+                _logger.info("智能评估扫描：入队 %d 个 Worker 任务", n)
+            # 清理：评估已结束（非 executing）的 pending/assigned 任务回收为 completed
+            settled = settle_tasks_for_finished_evals(session)
+            if settled:
+                _logger.info("任务回收：%d 个已结束评估的任务标记完成", settled)
+            # 审计兜底：agent 未上报决策日志时，平台按评估状态补录
+            added = supplement_decision_logs(session)
+            if added:
+                _logger.info("决策日志兜底：补录 %d 条", added)
+        finally:
+            session.close()
+    except Exception as exc:
+        _logger.warning("智能评估扫描失败（忽略）: %s", exc)
+    # analyst 兜底（ADR-0011）：末会话终态 10min 后平台催促 analyst 汇总报告，
+    # 催促落账后把评估交给 worker 触发（worker 在全终态时会转 analyst 路径）
+    nudge_ids: list[str] = []
+    try:
+        from agenteval.intelligent_eval.lifecycle import (
+            evals_needing_analyst_nudge,
+            record_analyst_nudge,
+        )
+
+        session = get_session()
+        try:
+            nudge_ids = evals_needing_analyst_nudge(session)
+            for eval_id in nudge_ids:
+                record_analyst_nudge(session, eval_id)
+        finally:
+            session.close()
+        if nudge_ids:
+            _logger.info("analyst 兜底：催促 %d 个评估汇总报告", len(nudge_ids))
+    except Exception as exc:
+        _logger.warning("analyst 兜底失败（忽略）: %s", exc)
+    # ADR-0011：触发 fire-and-forget，不阻塞 60s 扫描节奏（agent 最长跑 10min）
+    _fire_and_forget(trigger_worker(nudge_eval_ids=nudge_ids), "Worker")
+    _fire_and_forget(trigger_planner(), "Planner")
+
+
+class IntelligentEvalScheduler:
+    """扫描循环的 runtime 单例：持有任务句柄，web 层只做 start/stop。"""
+
+    def __init__(self) -> None:
+        self._task: Optional[asyncio.Task] = None
+
+    async def _loop(self) -> None:
+        while True:
+            scan_once()
+            await asyncio.sleep(SCAN_INTERVAL_SECONDS)
+
+    def start(self) -> None:
+        if self._task is None or self._task.done():
+            self._task = asyncio.create_task(self._loop())
+
+    async def stop(self) -> None:
+        if self._task is not None:
+            self._task.cancel()
+            try:
+                await self._task
+            except asyncio.CancelledError:
+                pass
+            self._task = None
+
+
+scheduler_runtime = IntelligentEvalScheduler()
diff --git a/backend/agenteval/intelligent_eval/task_queue.py b/backend/agenteval/intelligent_eval/task_queue.py
index 4ac560e..b6ffb57 100644
--- a/backend/agenteval/intelligent_eval/task_queue.py
+++ b/backend/agenteval/intelligent_eval/task_queue.py
@@ -224,7 +224,7 @@ def requeue_stale_assigned_tasks(session: Session) -> int:
     Returns:
         重新入队的任务数（不含被判 failed 的）。
     """
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
+    from agenteval.intelligent_eval.decision_logs import append_decision_log
 
     threshold = utc_now() - timedelta(minutes=STALE_ASSIGNED_MINUTES)
     stale = session.exec(
@@ -247,7 +247,7 @@ def requeue_stale_assigned_tasks(session: Session) -> int:
             task.completed_at = utc_now()
             task.updated_at = utc_now()
             abandoned += 1
-            create_decision_log(
+            append_decision_log(
                 task.eval_id,
                 "task_abandoned",
                 f"平台兜底：任务卡死重试 {task.attempts} 次仍未完成，置为失败",
diff --git a/backend/agenteval/web/app.py b/backend/agenteval/web/app.py
index 35f3ff4..be18879 100644
--- a/backend/agenteval/web/app.py
+++ b/backend/agenteval/web/app.py
@@ -1,17 +1,15 @@
 """FastAPI web backend for AgentEvalTool."""
 
-import asyncio
 import logging
-import time
 from contextlib import asynccontextmanager
 from pathlib import Path
-from typing import Optional
 
 from fastapi import Depends, FastAPI, WebSocket, WebSocketDisconnect
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse, JSONResponse
 
 from agenteval.config import get_settings
+from agenteval.intelligent_eval.scheduler import scheduler_runtime
 from agenteval.storage.db import get_session, init_db
 from agenteval.version import get_build_info, get_version
 from agenteval.web.deps import require_api_key
@@ -32,416 +30,10 @@ from agenteval.web.routers import (
 from agenteval.web.websocket import ws_manager
 
 
-def _supplement_decision_logs(session) -> int:
-    """Platform audit backfill for decision logs.
-
-    方案③的决策日志由 OpenClaw agent 上报（LLM 自主，尽力而为）——异常路径
-    （如卡死恢复后重试）agent 可能跳过上报，导致决策过程页面为空。这里按评估
-    状态推导决策并补录：
-
-    - EXECUTING：欠账（completed < estimated）补 execute_session，所有会话
-      完成后补 start_analysis。
-    - COMPLETED：历史评估/异常路径可能完全没有决策日志，回填 execute_session
-      （按 plan 时段逐条）+ start_analysis，让旧报告也有决策过程可看。
-
-    只补"该类型缺失"的，不重复；且只记录状态，不改变 agent 的实际执行。
-
-    Returns:
-        补录的决策日志条数。
-    """
-    from sqlmodel import select
-
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
-    from agenteval.intelligent_eval.models import IntelligentEvalStatus
-    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalDecisionLogDB, IntelligentEvalSessionDB
-
-    evals = session.exec(
-        select(IntelligentEvalDB).where(
-            IntelligentEvalDB.status.in_(
-                [
-                    IntelligentEvalStatus.EXECUTING.value,
-                    IntelligentEvalStatus.COMPLETED.value,
-                ]
-            )
-        )
-    ).all()
-
-    added = 0
-    for ev in evals:
-        plan = ev.get_plan() if ev.plan else {}
-        estimated = plan.get("estimated_sessions", 0)
-        sessions = session.exec(select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)).all()
-        completed = sum(1 for s in sessions if s.status == "completed")
-        types = {
-            x.decision_type
-            for x in session.exec(
-                select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
-            ).all()
-        }
-        if ev.status == IntelligentEvalStatus.EXECUTING.value:
-            if "execute_session" not in types and completed < estimated:
-                create_decision_log(
-                    ev.id,
-                    "execute_session",
-                    "平台兜底：时段欠账需执行会话",
-                    "platform",
-                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
-                    session,
-                )
-                added += 1
-            elif "start_analysis" not in types and sessions and completed >= estimated:
-                create_decision_log(
-                    ev.id,
-                    "start_analysis",
-                    "平台兜底：所有会话已完成开始分析",
-                    "platform",
-                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
-                    session,
-                )
-                added += 1
-        elif ev.status == IntelligentEvalStatus.COMPLETED.value:
-            # 历史回填：completed 评估决策日志全缺失时，按时段补 execute_session
-            if "execute_session" not in types:
-                slots = plan.get("time_distribution") or []
-                if slots:
-                    for slot in slots:
-                        create_decision_log(
-                            ev.id,
-                            "execute_session",
-                            f"平台兜底：时段{slot.get('time_slot', '')}执行会话（历史回填）",
-                            "platform",
-                            {
-                                "platform_supplemented": True,
-                                "time_slot": slot.get("time_slot"),
-                                "sessions": slot.get("sessions"),
-                                "completed": completed,
-                                "estimated": estimated,
-                            },
-                            session,
-                        )
-                        added += 1
-                else:
-                    create_decision_log(
-                        ev.id,
-                        "execute_session",
-                        "平台兜底：执行会话（历史回填）",
-                        "platform",
-                        {"platform_supplemented": True, "completed": completed, "estimated": estimated},
-                        session,
-                    )
-                    added += 1
-            if "start_analysis" not in types and sessions:
-                create_decision_log(
-                    ev.id,
-                    "start_analysis",
-                    "平台兜底：所有会话已完成开始分析（历史回填）",
-                    "platform",
-                    {"platform_supplemented": True, "completed": completed, "estimated": estimated},
-                    session,
-                )
-                added += 1
-    return added
-
-
-async def _trigger_intelligent_worker(nudge_eval_ids: Optional[list[str]] = None) -> bool:
-    """Trigger OpenClaw's headless agent to run the intelligent-eval worker skill.
-
-    方案③（避免外部 IM channel）：平台只当"触发闹钟"——有 pending 任务时，
-    用 `docker exec openclaw-eval openclaw agent --agent main -m
-    "agenteval-intelligent-worker"` 唤醒 agent，worker skill 由 OpenClaw agent
-    自主执行（决策/建会话/真实对话/上报，逻辑零改动）。`--deliver` 默认 false
-    因此不经 cron delivery channel。
-
-    ADR-0011 孤儿 agent 双管：
-    - 容器内命令包 `timeout 600`——agent 进程最多跑 10 分钟即被强杀；
-    - per-eval 触发冷却 10min（worker_trigger 决策日志记录）——agent 还在跑
-      的评估不重复触发，避免 60s 扫描节奏堆叠并发 agent。
-    nudge_eval_ids：analyst 兜底催促的评估（所有会话终态、无任务队列条目），
-    与候选任务评估合并去重后一同落账触发。
-
-    Returns:
-        True 若确实触发了 agent（存在冷却期外的服务对象）。
-    """
-    from agenteval.intelligent_eval.lifecycle import (
-        record_worker_triggers,
-        worker_trigger_candidates,
-    )
-
-    session = get_session()
-    try:
-        trigger_ids = sorted(set(worker_trigger_candidates(session)) | set(nudge_eval_ids or []))
-        if not trigger_ids:
-            return False
-        # 触发前落账（含后续失败也计入冷却），防止 agent 在跑期间重复触发
-        record_worker_triggers(session, trigger_ids)
-    finally:
-        session.close()
-
-    import subprocess
-
-    _logger = logging.getLogger("agenteval")
-    # 触发指令必须带"立即执行"语义：`openclaw agent` 无 cron state，若只发
-    # skill 名，agent 会按 worker skill 的"跨节拍"设计决策后等下一拍而死锁。
-    # 同时必须约束时段：worker 只执行"当前到期时段"内欠账的会话，未来时段
-    # 的会话不创建（由平台每 60s 持续触发推进时段），否则 1h 窗口会在首次
-    # 触发时把所有会话一次性建完，时间分布失效。
-    worker_msg = (
-        "执行 agenteval-intelligent-worker skill，立即完成当前任务，不要等待下一节拍："
-        "仅执行当前时间对应时段（time_distribution 中当前 offset 所在时段）内欠账的会话"
-        "——按评估 started_at 与当前时间精确判断当前时段，只创建该时段计划内的会话，"
-        "绝不创建未来时段的会话，未来时段到期后平台会再次触发你；"
-        "仅当所有时段计划内的会话都已达到终态（completed/failed/expired）时，"
-        "才调用 agenteval-intelligent-analyst skill 完成分析并生成报告；"
-        "若还有未到期的未来时段，保持等待、本次不做任何操作。"
-    )
-    try:
-        proc = await asyncio.to_thread(
-            subprocess.run,
-            [
-                "docker",
-                "exec",
-                "openclaw-eval",
-                # ADR-0011：容器内 timeout 强杀，agent 最长存活 10min（孤儿 agent 双管之一）
-                "timeout",
-                "600",
-                "openclaw",
-                "agent",
-                "--agent",
-                "main",
-                # 必须用独立 session：`--agent main` 复用 main 持久 session，多次触发
-                # 累积上下文缓存（~12 万 token）后 LLM 不再执行 worker skill 的 API
-                # 步骤，直接幻觉输出（如"评估 pending_approval"）而不取任务建会话。
-                "--session-id",
-                f"agenteval-worker-{int(time.time())}",
-                "-m",
-                worker_msg,
-                "--json",
-            ],
-            capture_output=True,
-            text=True,
-            timeout=660,  # 容器内 timeout 600 + 启动/回收余量
-        )
-        _logger.info("Worker 触发完成 exit=%s", proc.returncode)
-        if proc.returncode != 0:
-            _logger.warning("Worker 触发 stderr: %s", proc.stderr[-300:])
-            _record_worker_trigger_failure(f"exit={proc.returncode} stderr={proc.stderr[-200:]}")
-    except Exception as exc:
-        _logger.warning("Worker 触发失败（忽略）: %s", exc)
-        _record_worker_trigger_failure(str(exc))
-    return True
-
-
-def _record_worker_trigger_failure(error: str) -> None:
-    """ADR-0011：worker 触发失败落账到受影响评估（有待认领任务的评估）。"""
-    from agenteval.intelligent_eval.lifecycle import (
-        eval_ids_with_pending_worker_tasks,
-        record_trigger_failures,
-    )
-
-    session = get_session()
-    try:
-        eval_ids = eval_ids_with_pending_worker_tasks(session)
-        if eval_ids:
-            record_trigger_failures(session, channel="worker", eval_ids=eval_ids, error=error)
-    except Exception as exc:
-        logging.getLogger("agenteval").warning("触发失败落账失败（忽略）: %s", exc)
-    finally:
-        session.close()
-
-
-async def _trigger_intelligent_planner() -> bool:
-    """Trigger OpenClaw's headless agent to run the planner skill.
-
-    方案③只自动化了 executing（worker）→ completed（analyst），**缺少 planning
-    阶段的 planner 触发**——新建或被打回的评估会永远停在 planning。这里对
-    planning 状态评估触发 `agenteval-intelligent-planner` skill：planner 自会
-    拉取 planning 评估列表、读取四件套、产出粗计划并 PUT /plan 提交（planner
-    skill 定义见 OpenClaw workspace skills）。处理完评估离开 planning 后不再触发。
-
-    Returns:
-        True 若确实触发了 agent（存在 planning 评估）。
-    """
-    # ADR-0011：触发前先落账 planner_trigger（含后续失败也计入双闸），
-    # 0 个 planning 评估时不触发。
-    from agenteval.intelligent_eval.lifecycle import record_planner_triggers
-
-    session = get_session()
-    try:
-        planning_count = record_planner_triggers(session)
-    finally:
-        session.close()
-    if planning_count == 0:
-        return False
-
-    import subprocess
-
-    _logger = logging.getLogger("agenteval")
-    # 同 worker：`openclaw agent` 无 cron state，须带"立即完成"语义，否则 planner
-    # 会"决策后等下一拍"而死锁。
-    planner_msg = (
-        "执行 agenteval-intelligent-planner skill，立即完成当前任务，不要等待下一节拍："
-        "为 planning 状态的智能评估读取输入、产出粗计划并提交平台审批。"
-    )
-    try:
-        proc = await asyncio.to_thread(
-            subprocess.run,
-            [
-                "docker",
-                "exec",
-                "openclaw-eval",
-                # ADR-0011：容器内 timeout 强杀，agent 最长存活 10min（孤儿 agent 双管之一）
-                "timeout",
-                "600",
-                "openclaw",
-                "agent",
-                "--agent",
-                "main",
-                # 同 worker：独立 session 避免 main 持久 session 上下文缓存污染
-                "--session-id",
-                f"agenteval-planner-{int(time.time())}",
-                "-m",
-                planner_msg,
-                "--json",
-            ],
-            capture_output=True,
-            text=True,
-            timeout=660,  # 容器内 timeout 600 + 启动/回收余量
-        )
-        _logger.info("Planner 触发完成 exit=%s", proc.returncode)
-        if proc.returncode != 0:
-            _logger.warning("Planner 触发 stderr: %s", proc.stderr[-300:])
-            _record_planner_trigger_failure(f"exit={proc.returncode} stderr={proc.stderr[-200:]}")
-    except Exception as exc:
-        _logger.warning("Planner 触发失败（忽略）: %s", exc)
-        _record_planner_trigger_failure(str(exc))
-    return True
-
-
-def _record_planner_trigger_failure(error: str) -> None:
-    """ADR-0011：planner 触发失败落账到所有 planning 评估。"""
-    from sqlmodel import select
-
-    from agenteval.intelligent_eval.lifecycle import record_trigger_failures
-    from agenteval.intelligent_eval.models import IntelligentEvalStatus
-    from agenteval.storage.db import IntelligentEvalDB
-
-    session = get_session()
-    try:
-        eval_ids = [
-            row.id
-            for row in session.exec(
-                select(IntelligentEvalDB).where(IntelligentEvalDB.status == IntelligentEvalStatus.PLANNING.value)
-            ).all()
-        ]
-        if eval_ids:
-            record_trigger_failures(session, channel="planner", eval_ids=eval_ids, error=error)
-    except Exception as exc:
-        logging.getLogger("agenteval").warning("触发失败落账失败（忽略）: %s", exc)
-    finally:
-        session.close()
-
-
-def _fire_and_forget(coro, name: str) -> None:
-    """ADR-0011：触发改为 fire-and-forget——agent 最长跑 10min，await 会把
-    60s 扫描节奏拖到 10min+。派生 asyncio task，异常在完成回调中记录
-    （触发失败已由触发函数自身落账 trigger_failed）。"""
-
-    def _done(task: asyncio.Task) -> None:
-        if task.cancelled():
-            return
-        exc = task.exception()
-        if exc:
-            logging.getLogger("agenteval").warning("%s 触发任务异常（忽略）: %s", name, exc)
-
-    asyncio.create_task(coro).add_done_callback(_done)
-
-
-async def _intelligent_eval_scan_loop() -> None:
-    """Scan intelligent evals, enqueue tasks, and trigger planner/worker.
-
-    v1.1.0 缺陷修复：`scan_and_enqueue_tasks` 此前没有调度点，OpenClaw Worker
-    每分钟唤醒却永远取不到任务。平台启动后每 60s：
-    ① 有 planning 评估则触发 OpenClaw planner skill 产出粗计划（planning→待审批）；
-    ② 扫描 executing 的评估入队；③ 若有 pending 任务则触发 worker skill 执行会话/
-    分析（方案③，免外部 channel）。失败不阻断（下次循环继续）。
-    """
-    _logger = logging.getLogger("agenteval")
-    while True:
-        try:
-            session = get_session()
-            try:
-                from agenteval.intelligent_eval.lifecycle import (
-                    enforce_executing_ceiling,
-                    enforce_planning_gates,
-                    enforce_trigger_failure_gates,
-                    expire_stale_running_sessions,
-                )
-                from agenteval.intelligent_eval.task_queue import (
-                    requeue_stale_assigned_tasks,
-                    scan_and_enqueue_tasks,
-                    settle_tasks_for_finished_evals,
-                )
-
-                r = requeue_stale_assigned_tasks(session)
-                if r:
-                    _logger.info("卡死恢复：%d 个 assigned 任务重新入队", r)
-                x = expire_stale_running_sessions(session)
-                if x:
-                    _logger.info("会话过期：%d 个 running 会话 60 分钟无新轮次，置为 expired", x)
-                g = enforce_planning_gates(session)
-                if g:
-                    _logger.info("planning 双闸：%d 个评估超限判失败", g)
-                c = enforce_executing_ceiling(session)
-                if c:
-                    _logger.info("executing 兜底：%d 个评估超窗判失败", c)
-                f = enforce_trigger_failure_gates(session)
-                if f:
-                    _logger.info("触发失败判死：%d 个评估连续触发失败超限判失败", f)
-                n = scan_and_enqueue_tasks(session)
-                if n:
-                    _logger.info("智能评估扫描：入队 %d 个 Worker 任务", n)
-                # 清理：评估已结束（非 executing）的 pending/assigned 任务回收为 completed
-                settled = settle_tasks_for_finished_evals(session)
-                if settled:
-                    _logger.info("任务回收：%d 个已结束评估的任务标记完成", settled)
-                # 审计兜底：agent 未上报决策日志时，平台按评估状态补录
-                added = _supplement_decision_logs(session)
-                if added:
-                    _logger.info("决策日志兜底：补录 %d 条", added)
-            finally:
-                session.close()
-        except Exception as exc:
-            logging.getLogger("agenteval").warning("智能评估扫描失败（忽略）: %s", exc)
-        # analyst 兜底（ADR-0011）：末会话终态 10min 后平台催促 analyst 汇总报告，
-        # 催促落账后把评估交给 worker 触发（worker 在全终态时会转 analyst 路径）
-        nudge_ids: list[str] = []
-        try:
-            from agenteval.intelligent_eval.lifecycle import (
-                evals_needing_analyst_nudge,
-                record_analyst_nudge,
-            )
-
-            session = get_session()
-            try:
-                nudge_ids = evals_needing_analyst_nudge(session)
-                for eval_id in nudge_ids:
-                    record_analyst_nudge(session, eval_id)
-            finally:
-                session.close()
-            if nudge_ids:
-                logging.getLogger("agenteval").info("analyst 兜底：催促 %d 个评估汇总报告", len(nudge_ids))
-        except Exception as exc:
-            logging.getLogger("agenteval").warning("analyst 兜底失败（忽略）: %s", exc)
-        # ADR-0011：触发 fire-and-forget，不阻塞 60s 扫描节奏（agent 最长跑 10min）
-        _fire_and_forget(_trigger_intelligent_worker(nudge_eval_ids=nudge_ids), "Worker")
-        _fire_and_forget(_trigger_intelligent_planner(), "Planner")
-        await asyncio.sleep(60)
-
-
 @asynccontextmanager
 async def lifespan(_: FastAPI):
     init_db()
-    scan_task = asyncio.create_task(_intelligent_eval_scan_loop())
+    scheduler_runtime.start()
     # 恢复耐久 Campaign/智能作业，并清理无法安全重放的中断运行（尽力而为，不阻断启动）。
     try:
         session = get_session()
@@ -474,12 +66,8 @@ async def lifespan(_: FastAPI):
     except Exception as exc:
         logging.getLogger("agenteval").warning("启动清理失败（忽略）: %s", exc)
     yield
-    # 优雅停止后台扫描，再停其他进程内任务。
-    scan_task.cancel()
-    try:
-        await scan_task
-    except asyncio.CancelledError:
-        pass
+    # 优雅停止智能评估扫描调度，再停其他进程内任务。
+    await scheduler_runtime.stop()
     # 优雅停止所有进程内任务：先停活动调度循环，再停在跑的评测运行，
     # 最后停活动智能作业（分析 / 周期对比）和 judge 复核。
     try:
diff --git a/tests/integration/test_decision_log_immutability.py b/tests/integration/test_decision_log_immutability.py
index f4597be..00dda82 100644
--- a/tests/integration/test_decision_log_immutability.py
+++ b/tests/integration/test_decision_log_immutability.py
@@ -132,3 +132,45 @@ def test_decision_log_dedupes_identical_entries(
         )
     ).all()
     assert len(rows) == 1, f"expected dedup, got {len(rows)} rows"
+
+
+def test_append_decision_log_always_appends_identical_context(db_session: Session):
+    """平台落账入口不去重：相同 context 也总是追加（每次落账都是新事实）。"""
+    from agenteval.intelligent_eval.decision_logs import append_decision_log
+
+    eval_db = _make_eval(db_session)
+    ctx = {"platform_supplemented": True, "attempt": 1}
+    for _ in range(3):
+        append_decision_log(eval_db.id, "worker_trigger", "落账", "platform", ctx, db_session)
+
+    rows = db_session.exec(
+        select(IntelligentEvalDecisionLogDB).where(
+            IntelligentEvalDecisionLogDB.eval_id == eval_db.id
+        )
+    ).all()
+    assert len(rows) == 3, f"append 不应去重，得到 {len(rows)} 行"
+
+
+def test_append_decision_log_raises_for_unknown_eval(db_session: Session):
+    """append 与 create 一样校验评估存在性。"""
+    from agenteval.intelligent_eval.decision_logs import append_decision_log
+
+    with pytest.raises(LookupError):
+        append_decision_log("no-such-eval", "worker_trigger", "r", "platform", {}, db_session)
+
+
+def test_count_decisions_counts_by_eval_and_type(db_session: Session):
+    """count_decisions 按 eval + 类型计数（闸门与 attempt 落账的计数原语）。"""
+    from agenteval.intelligent_eval.decision_logs import append_decision_log, count_decisions
+
+    eval_db = _make_eval(db_session)
+    other = _make_eval(db_session)
+    for _ in range(2):
+        append_decision_log(eval_db.id, "planner_trigger", "r", "platform", {"n": 1}, db_session)
+    append_decision_log(eval_db.id, "worker_trigger", "r", "platform", {"n": 2}, db_session)
+    append_decision_log(other.id, "planner_trigger", "r", "platform", {"n": 3}, db_session)
+
+    assert count_decisions(eval_db.id, "planner_trigger", db_session) == 2
+    assert count_decisions(eval_db.id, "worker_trigger", db_session) == 1
+    assert count_decisions(other.id, "planner_trigger", db_session) == 1
+    assert count_decisions(eval_db.id, "trigger_failed", db_session) == 0
diff --git a/tests/integration/test_intelligent_eval_scan_scheduler.py b/tests/integration/test_intelligent_eval_scan_scheduler.py
index a422049..389d7b8 100644
--- a/tests/integration/test_intelligent_eval_scan_scheduler.py
+++ b/tests/integration/test_intelligent_eval_scan_scheduler.py
@@ -1,9 +1,12 @@
-"""Lifespan scan-loop test (v1.1.0 defect fix).
+"""Lifespan scheduler wiring test (v1.1.0 defect fix).
 
 `scan_and_enqueue_tasks` previously had no scheduler — the OpenClaw Worker
-wakes every minute but could never pull a task. The lifespan now starts an
-asyncio background task that scans executing evals every 60s. This test
-verifies that on application startup the scan is actually invoked.
+wakes every minute but could never pull a task. The lifespan now starts the
+intelligent-eval scheduler runtime that scans executing evals every 60s.
+This test verifies that on application startup the scan is actually invoked.
+
+编排逻辑本身（watchdog/入队/触发）的测试在
+``tests/unit/test_intelligent_eval_scheduler.py``，直接驱动 ``scan_once``。
 """
 
 from unittest.mock import MagicMock
@@ -13,6 +16,7 @@ from fastapi.testclient import TestClient
 
 def test_lifespan_starts_scan_loop(monkeypatch):
     """Lifespan startup must invoke the intelligent-eval scan loop once."""
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
     import agenteval.intelligent_eval.task_queue as tq
     import agenteval.web.app as app_mod
 
@@ -25,7 +29,7 @@ def test_lifespan_starts_scan_loop(monkeypatch):
 
     # The scan loop calls get_session() to open a DB session; replace it with a
     # no-op mock so the test does not touch the real SQLite file.
-    monkeypatch.setattr(app_mod, "get_session", lambda: MagicMock())
+    monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
     monkeypatch.setattr(tq, "scan_and_enqueue_tasks", fake_scan)
 
     with TestClient(app_mod.app) as client:
@@ -33,303 +37,3 @@ def test_lifespan_starts_scan_loop(monkeypatch):
 
     # The background task runs immediately (before its first 60s sleep).
     assert calls, "scan_and_enqueue_tasks should have been invoked on startup"
-
-
-def _patch_worker_candidates(monkeypatch, candidates: list[str]):
-    """ADR-0011：worker 触发的服务对象由冷却过滤后的候选决定（替代原 _has_pending_task）。"""
-    from unittest.mock import MagicMock
-
-    import agenteval.intelligent_eval.lifecycle as lifecycle_mod
-    import agenteval.web.app as app_mod
-
-    monkeypatch.setattr(lifecycle_mod, "worker_trigger_candidates", lambda session: candidates)
-    monkeypatch.setattr(lifecycle_mod, "record_worker_triggers", lambda session, ids: None)
-    monkeypatch.setattr(app_mod, "get_session", lambda: MagicMock())
-
-
-def test_trigger_worker_skips_when_no_pending(monkeypatch):
-    """No pending task → no docker exec invocation."""
-    import asyncio
-    import subprocess
-    from unittest.mock import MagicMock
-
-    import agenteval.web.app as app_mod
-
-    calls: list = []
-    _patch_worker_candidates(monkeypatch, [])
-
-    def fake_run(cmd, **kwargs):
-        calls.append(cmd)
-        return MagicMock(returncode=0, stderr="")
-
-    monkeypatch.setattr(subprocess, "run", fake_run)
-
-    assert asyncio.run(app_mod._trigger_intelligent_worker()) is False
-    assert not calls, "should not invoke docker exec when queue is empty"
-
-
-def test_trigger_worker_calls_docker_exec(monkeypatch):
-    """Pending task present → invoke `docker exec openclaw-eval openclaw agent`."""
-    import asyncio
-    import subprocess
-    from unittest.mock import MagicMock
-
-    import agenteval.web.app as app_mod
-
-    calls: list = []
-    _patch_worker_candidates(monkeypatch, ["ev-1"])
-
-    def fake_run(cmd, **kwargs):
-        calls.append(cmd)
-        return MagicMock(returncode=0, stderr="")
-
-    monkeypatch.setattr(subprocess, "run", fake_run)
-
-    assert asyncio.run(app_mod._trigger_intelligent_worker()) is True
-    assert calls, "docker exec should be invoked"
-    joined = " ".join(calls[0])
-    assert "docker" in joined and "openclaw" in joined
-    assert "agenteval-intelligent-worker" in joined
-    assert "--agent" in joined and "main" in joined
-    # 独立 session：避免 main 持久 session 上下文缓存污染导致 worker 幻觉不执行
-    assert "--session-id" in joined
-    assert "agenteval-worker-" in joined
-    # ADR-0011 孤儿 agent 双管：容器内 timeout 强杀
-    assert "timeout" in joined and "600" in joined
-
-
-def test_trigger_worker_msg_has_execute_semantics(monkeypatch):
-    """The trigger message must instruct immediate execution (no cron state).
-
-    `openclaw agent` has no cron state; a bare skill name makes the worker
-    skill "decide then wait for the next tick", deadlocking. The message must
-    say "立即完成当前任务 / 不要等待下一节拍".
-    """
-    import asyncio
-    import subprocess
-    from unittest.mock import MagicMock
-
-    import agenteval.web.app as app_mod
-
-    calls: list = []
-    _patch_worker_candidates(monkeypatch, ["ev-1"])
-
-    def fake_run(cmd, **kwargs):
-        calls.append(cmd)
-        return MagicMock(returncode=0, stderr="")
-
-    monkeypatch.setattr(subprocess, "run", fake_run)
-
-    asyncio.run(app_mod._trigger_intelligent_worker())
-    joined = " ".join(calls[0])
-    assert "不要等待下一节拍" in joined
-    assert "立即完成当前任务" in joined
-    assert "agenteval-intelligent-worker" in joined
-    assert "agenteval-intelligent-analyst" in joined
-
-
-def test_trigger_planner_skips_when_no_planning(monkeypatch):
-    """No planning eval → no docker exec invocation."""
-    import asyncio
-    import subprocess
-    from unittest.mock import MagicMock
-
-    import agenteval.intelligent_eval.lifecycle as lifecycle_mod
-    import agenteval.web.app as app_mod
-
-    calls: list = []
-    # ADR-0011：planner 触发前置计数落账（record_planner_triggers），0 时不触发
-    monkeypatch.setattr(lifecycle_mod, "record_planner_triggers", lambda session: 0)
-    monkeypatch.setattr(app_mod, "get_session", lambda: MagicMock())
-
-    def fake_run(cmd, **kwargs):
-        calls.append(cmd)
-        return MagicMock(returncode=0, stderr="")
-
-    monkeypatch.setattr(subprocess, "run", fake_run)
-
-    assert asyncio.run(app_mod._trigger_intelligent_planner()) is False
-    assert not calls, "should not invoke docker exec when no planning eval"
-
-
-def test_trigger_planner_calls_docker_exec(monkeypatch):
-    """Planning eval present → invoke `docker exec openclaw-eval openclaw agent` planner skill."""
-    import asyncio
-    import subprocess
-    from unittest.mock import MagicMock
-
-    import agenteval.intelligent_eval.lifecycle as lifecycle_mod
-    import agenteval.web.app as app_mod
-
-    calls: list = []
-    monkeypatch.setattr(lifecycle_mod, "record_planner_triggers", lambda session: 1)
-    monkeypatch.setattr(app_mod, "get_session", lambda: MagicMock())
-
-    def fake_run(cmd, **kwargs):
-        calls.append(cmd)
-        return MagicMock(returncode=0, stderr="")
-
-    monkeypatch.setattr(subprocess, "run", fake_run)
-
-    assert asyncio.run(app_mod._trigger_intelligent_planner()) is True
-    assert calls, "docker exec should be invoked"
-    joined = " ".join(calls[0])
-    assert "docker" in joined and "openclaw" in joined
-    assert "agenteval-intelligent-planner" in joined
-    # 独立 session（同 worker 防上下文缓存污染）
-    assert "--session-id" in joined
-    assert "agenteval-planner-" in joined
-    assert "立即完成当前任务" in joined
-    assert "不要等待下一节拍" in joined
-
-
-def test_supplement_execute_session_log(monkeypatch, db_session):
-    """Executing eval with deficit and no execute_session log → platform backfills."""
-    import agenteval.web.app as app_mod
-    from agenteval.intelligent_eval.models import IntelligentEvalStatus
-    from agenteval.storage.db import IntelligentEvalDB
-
-    ev = IntelligentEvalDB(
-        name="supp-eval",
-        target_id="t1",
-        status=IntelligentEvalStatus.EXECUTING.value,
-        started_at=__import__("agenteval.storage.db", fromlist=["utc_now"]).utc_now(),
-    )
-    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
-    db_session.add(ev)
-    db_session.commit()
-
-    assert app_mod._supplement_decision_logs(db_session) == 1
-    # second call: already backfilled → 0
-    assert app_mod._supplement_decision_logs(db_session) == 0
-
-
-def test_supplement_start_analysis_log(monkeypatch, db_session):
-    """Executing eval with all sessions completed and no start_analysis → backfill."""
-    import agenteval.web.app as app_mod
-    from agenteval.intelligent_eval.models import IntelligentEvalStatus
-    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB, utc_now
-
-    ev = IntelligentEvalDB(
-        name="supp-eval2",
-        target_id="t1",
-        status=IntelligentEvalStatus.EXECUTING.value,
-        started_at=utc_now(),
-    )
-    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
-    db_session.add(ev)
-    db_session.commit()
-    s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
-    db_session.add(s)
-    db_session.commit()
-
-    assert app_mod._supplement_decision_logs(db_session) == 1
-
-
-def test_supplement_completed_backfill(monkeypatch, db_session):
-    """Completed eval with no decision logs → backfill execute_session + start_analysis."""
-    import agenteval.web.app as app_mod
-    from agenteval.intelligent_eval.models import IntelligentEvalStatus
-    from agenteval.storage.db import (
-        IntelligentEvalDB,
-        IntelligentEvalDecisionLogDB,
-        IntelligentEvalSessionDB,
-        utc_now,
-    )
-    from sqlmodel import select
-
-    ev = IntelligentEvalDB(
-        name="supp-completed",
-        target_id="t1",
-        status=IntelligentEvalStatus.COMPLETED.value,
-        started_at=utc_now(),
-    )
-    ev.set_plan(
-        {
-            "time_distribution": [
-                {"time_slot": "0-1h", "sessions": 1},
-                {"time_slot": "1-2h", "sessions": 1},
-            ],
-            "estimated_sessions": 2,
-        }
-    )
-    db_session.add(ev)
-    db_session.commit()
-    for _ in range(2):
-        s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
-        db_session.add(s)
-    db_session.commit()
-
-    # 首轮：2 时段 → 2 条 execute_session + 1 条 start_analysis
-    assert app_mod._supplement_decision_logs(db_session) == 3
-    # 幂等：二次调用不再补
-    assert app_mod._supplement_decision_logs(db_session) == 0
-
-    logs = db_session.exec(
-        select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
-    ).all()
-    types = sorted(x.decision_type for x in logs)
-    assert types == ["execute_session", "execute_session", "start_analysis"]
-    assert all(x.cron_id == "platform" for x in logs)
-    assert all(x.get_context().get("platform_supplemented") for x in logs)
-
-
-def test_supplement_completed_skips_when_already_logged(monkeypatch, db_session):
-    """Completed eval that already has worker-reported logs is not duplicated."""
-    import agenteval.web.app as app_mod
-    from agenteval.intelligent_eval.models import IntelligentEvalStatus
-    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB, utc_now
-
-    ev = IntelligentEvalDB(
-        name="supp-completed2",
-        target_id="t1",
-        status=IntelligentEvalStatus.COMPLETED.value,
-        started_at=utc_now(),
-    )
-    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
-    db_session.add(ev)
-    db_session.commit()
-    s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
-    db_session.add(s)
-    db_session.commit()
-
-    from agenteval.intelligent_eval.decision_logs import create_decision_log
-
-    # 已有 worker 正常上报的两条日志
-    create_decision_log(ev.id, "execute_session", "时段0-1h欠账1个会话，需要执行会话", "manual-run-1", {}, db_session)
-    create_decision_log(ev.id, "start_analysis", "所有会话已完成，开始分析", "manual-run-1", {}, db_session)
-
-    assert app_mod._supplement_decision_logs(db_session) == 0
-
-
-def test_fire_and_forget_does_not_block_and_logs_errors(monkeypatch):
-    """ADR-0011：触发派生 asyncio task 后台执行，异常在完成回调记录而不抛出。"""
-    import asyncio
-    import logging
-
-    import agenteval.web.app as app_mod
-
-    async def boom():
-        await asyncio.sleep(0)
-        raise RuntimeError("触发爆炸")
-
-    async def main():
-        app_mod._fire_and_forget(boom(), "Worker")
-        await asyncio.sleep(0.01)  # 让被派生的任务及其完成回调执行
-        return "loop-continues"
-
-    # 直接挂 handler 到 agenteval logger：不依赖 caplog（全量跑时 alembic 迁移
-    # 用例的 fileConfig 会 disable_existing_loggers，把 agenteval logger 关掉）
-    records: list[logging.LogRecord] = []
-    handler = logging.Handler()
-    handler.emit = records.append
-    logger = logging.getLogger("agenteval")
-    was_disabled = logger.disabled
-    logger.disabled = False
-    logger.addHandler(handler)
-    try:
-        assert asyncio.run(main()) == "loop-continues"
-    finally:
-        logger.removeHandler(handler)
-        logger.disabled = was_disabled
-    assert any("触发爆炸" in r.getMessage() for r in records)
diff --git a/tests/unit/test_intelligent_eval_scheduler.py b/tests/unit/test_intelligent_eval_scheduler.py
new file mode 100644
index 0000000..d79963c
--- /dev/null
+++ b/tests/unit/test_intelligent_eval_scheduler.py
@@ -0,0 +1,331 @@
+"""Unit tests for the intelligent-eval scheduler runtime.
+
+从 tests/integration/test_intelligent_eval_scan_scheduler.py 迁移：
+scheduler 从 web/app.py 抽出后，编排逻辑直接通过其 interface
+（``scan_once`` / ``trigger_worker`` / ``trigger_planner``）驱动，
+不再隔着 TestClient 与 60s 循环。lifespan 启停接线测试留在 integration。
+"""
+
+import asyncio
+import logging
+import subprocess
+from unittest.mock import MagicMock
+
+
+def _patch_worker_candidates(monkeypatch, candidates: list[str]):
+    """ADR-0011：worker 触发的服务对象由冷却过滤后的候选决定（替代原 _has_pending_task）。"""
+    import agenteval.intelligent_eval.lifecycle as lifecycle_mod
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+
+    monkeypatch.setattr(lifecycle_mod, "worker_trigger_candidates", lambda session: candidates)
+    monkeypatch.setattr(lifecycle_mod, "record_worker_triggers", lambda session, ids: None)
+    monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
+
+
+def test_trigger_worker_skips_when_no_pending(monkeypatch):
+    """No pending task → no docker exec invocation."""
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+
+    calls: list = []
+    _patch_worker_candidates(monkeypatch, [])
+
+    def fake_run(cmd, **kwargs):
+        calls.append(cmd)
+        return MagicMock(returncode=0, stderr="")
+
+    monkeypatch.setattr(subprocess, "run", fake_run)
+
+    assert asyncio.run(scheduler_mod.trigger_worker()) is False
+    assert not calls, "should not invoke docker exec when queue is empty"
+
+
+def test_trigger_worker_calls_docker_exec(monkeypatch):
+    """Pending task present → invoke `docker exec openclaw-eval openclaw agent`."""
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+
+    calls: list = []
+    _patch_worker_candidates(monkeypatch, ["ev-1"])
+
+    def fake_run(cmd, **kwargs):
+        calls.append(cmd)
+        return MagicMock(returncode=0, stderr="")
+
+    monkeypatch.setattr(subprocess, "run", fake_run)
+
+    assert asyncio.run(scheduler_mod.trigger_worker()) is True
+    assert calls, "docker exec should be invoked"
+    joined = " ".join(calls[0])
+    assert "docker" in joined and "openclaw" in joined
+    assert "agenteval-intelligent-worker" in joined
+    assert "--agent" in joined and "main" in joined
+    # 独立 session：避免 main 持久 session 上下文缓存污染导致 worker 幻觉不执行
+    assert "--session-id" in joined
+    assert "agenteval-worker-" in joined
+    # ADR-0011 孤儿 agent 双管：容器内 timeout 强杀
+    assert "timeout" in joined and "600" in joined
+
+
+def test_trigger_worker_msg_has_execute_semantics(monkeypatch):
+    """The trigger message must instruct immediate execution (no cron state).
+
+    `openclaw agent` has no cron state; a bare skill name makes the worker
+    skill "decide then wait for the next tick", deadlocking. The message must
+    say "立即完成当前任务 / 不要等待下一节拍".
+    """
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+
+    calls: list = []
+    _patch_worker_candidates(monkeypatch, ["ev-1"])
+
+    def fake_run(cmd, **kwargs):
+        calls.append(cmd)
+        return MagicMock(returncode=0, stderr="")
+
+    monkeypatch.setattr(subprocess, "run", fake_run)
+
+    asyncio.run(scheduler_mod.trigger_worker())
+    joined = " ".join(calls[0])
+    assert "不要等待下一节拍" in joined
+    assert "立即完成当前任务" in joined
+    assert "agenteval-intelligent-worker" in joined
+    assert "agenteval-intelligent-analyst" in joined
+
+
+def test_trigger_planner_skips_when_no_planning(monkeypatch):
+    """No planning eval → no docker exec invocation."""
+    import agenteval.intelligent_eval.lifecycle as lifecycle_mod
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+
+    calls: list = []
+    # ADR-0011：planner 触发前置计数落账（record_planner_triggers），0 时不触发
+    monkeypatch.setattr(lifecycle_mod, "record_planner_triggers", lambda session: 0)
+    monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
+
+    def fake_run(cmd, **kwargs):
+        calls.append(cmd)
+        return MagicMock(returncode=0, stderr="")
+
+    monkeypatch.setattr(subprocess, "run", fake_run)
+
+    assert asyncio.run(scheduler_mod.trigger_planner()) is False
+    assert not calls, "should not invoke docker exec when no planning eval"
+
+
+def test_trigger_planner_calls_docker_exec(monkeypatch):
+    """Planning eval present → invoke `docker exec openclaw-eval openclaw agent` planner skill."""
+    import agenteval.intelligent_eval.lifecycle as lifecycle_mod
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+
+    calls: list = []
+    monkeypatch.setattr(lifecycle_mod, "record_planner_triggers", lambda session: 1)
+    monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
+
+    def fake_run(cmd, **kwargs):
+        calls.append(cmd)
+        return MagicMock(returncode=0, stderr="")
+
+    monkeypatch.setattr(subprocess, "run", fake_run)
+
+    assert asyncio.run(scheduler_mod.trigger_planner()) is True
+    assert calls, "docker exec should be invoked"
+    joined = " ".join(calls[0])
+    assert "docker" in joined and "openclaw" in joined
+    assert "agenteval-intelligent-planner" in joined
+    # 独立 session（同 worker 防上下文缓存污染）
+    assert "--session-id" in joined
+    assert "agenteval-planner-" in joined
+    assert "立即完成当前任务" in joined
+    assert "不要等待下一节拍" in joined
+
+
+def test_scan_once_orchestrates_full_tick(monkeypatch):
+    """scan_once 一个节拍按原顺序执行 watchdog → 入队 → 回收 → 补录 → 催促 → 触发。"""
+    import agenteval.intelligent_eval.decision_logs as dl_mod
+    import agenteval.intelligent_eval.lifecycle as lifecycle_mod
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+    import agenteval.intelligent_eval.task_queue as tq_mod
+
+    calls: list[str] = []
+    monkeypatch.setattr(scheduler_mod, "get_session", lambda: MagicMock())
+
+    for name in ("requeue_stale_assigned_tasks", "scan_and_enqueue_tasks", "settle_tasks_for_finished_evals"):
+        monkeypatch.setattr(tq_mod, name, lambda session, _n=name: calls.append(_n) or 0)
+    for name in (
+        "expire_stale_running_sessions",
+        "enforce_planning_gates",
+        "enforce_executing_ceiling",
+        "enforce_trigger_failure_gates",
+    ):
+        monkeypatch.setattr(lifecycle_mod, name, lambda session, _n=name: calls.append(_n) or 0)
+    monkeypatch.setattr(dl_mod, "supplement_decision_logs", lambda session: calls.append("supplement") or 0)
+    monkeypatch.setattr(lifecycle_mod, "evals_needing_analyst_nudge", lambda session: calls.append("nudge") or [])
+
+    fired: list[str] = []
+
+    def fake_fire_and_forget(coro, name):
+        fired.append(name)
+        coro.close()  # 不真正执行触发（避免 docker exec）
+
+    monkeypatch.setattr(scheduler_mod, "_fire_and_forget", fake_fire_and_forget)
+
+    scheduler_mod.scan_once()
+
+    assert calls == [
+        "requeue_stale_assigned_tasks",
+        "expire_stale_running_sessions",
+        "enforce_planning_gates",
+        "enforce_executing_ceiling",
+        "enforce_trigger_failure_gates",
+        "scan_and_enqueue_tasks",
+        "settle_tasks_for_finished_evals",
+        "supplement",
+        "nudge",
+    ]
+    assert fired == ["Worker", "Planner"]
+
+
+def test_supplement_execute_session_log(monkeypatch, db_session):
+    """Executing eval with deficit and no execute_session log → platform backfills."""
+    from agenteval.intelligent_eval.decision_logs import supplement_decision_logs
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB, utc_now
+
+    ev = IntelligentEvalDB(
+        name="supp-eval",
+        target_id="t1",
+        status=IntelligentEvalStatus.EXECUTING.value,
+        started_at=utc_now(),
+    )
+    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
+    db_session.add(ev)
+    db_session.commit()
+
+    assert supplement_decision_logs(db_session) == 1
+    # second call: already backfilled → 0
+    assert supplement_decision_logs(db_session) == 0
+
+
+def test_supplement_start_analysis_log(monkeypatch, db_session):
+    """Executing eval with all sessions completed and no start_analysis → backfill."""
+    from agenteval.intelligent_eval.decision_logs import supplement_decision_logs
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB, utc_now
+
+    ev = IntelligentEvalDB(
+        name="supp-eval2",
+        target_id="t1",
+        status=IntelligentEvalStatus.EXECUTING.value,
+        started_at=utc_now(),
+    )
+    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
+    db_session.add(ev)
+    db_session.commit()
+    s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
+    db_session.add(s)
+    db_session.commit()
+
+    assert supplement_decision_logs(db_session) == 1
+
+
+def test_supplement_completed_backfill(monkeypatch, db_session):
+    """Completed eval with no decision logs → backfill execute_session + start_analysis."""
+    from agenteval.intelligent_eval.decision_logs import supplement_decision_logs
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import (
+        IntelligentEvalDB,
+        IntelligentEvalDecisionLogDB,
+        IntelligentEvalSessionDB,
+        utc_now,
+    )
+    from sqlmodel import select
+
+    ev = IntelligentEvalDB(
+        name="supp-completed",
+        target_id="t1",
+        status=IntelligentEvalStatus.COMPLETED.value,
+        started_at=utc_now(),
+    )
+    ev.set_plan(
+        {
+            "time_distribution": [
+                {"time_slot": "0-1h", "sessions": 1},
+                {"time_slot": "1-2h", "sessions": 1},
+            ],
+            "estimated_sessions": 2,
+        }
+    )
+    db_session.add(ev)
+    db_session.commit()
+    for _ in range(2):
+        s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
+        db_session.add(s)
+    db_session.commit()
+
+    # 首轮：2 时段 → 2 条 execute_session + 1 条 start_analysis
+    assert supplement_decision_logs(db_session) == 3
+    # 幂等：二次调用不再补
+    assert supplement_decision_logs(db_session) == 0
+
+    logs = db_session.exec(
+        select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
+    ).all()
+    types = sorted(x.decision_type for x in logs)
+    assert types == ["execute_session", "execute_session", "start_analysis"]
+    assert all(x.cron_id == "platform" for x in logs)
+    assert all(x.get_context().get("platform_supplemented") for x in logs)
+
+
+def test_supplement_completed_skips_when_already_logged(monkeypatch, db_session):
+    """Completed eval that already has worker-reported logs is not duplicated."""
+    from agenteval.intelligent_eval.decision_logs import create_decision_log, supplement_decision_logs
+    from agenteval.intelligent_eval.models import IntelligentEvalStatus
+    from agenteval.storage.db import IntelligentEvalDB, IntelligentEvalSessionDB, utc_now
+
+    ev = IntelligentEvalDB(
+        name="supp-completed2",
+        target_id="t1",
+        status=IntelligentEvalStatus.COMPLETED.value,
+        started_at=utc_now(),
+    )
+    ev.set_plan({"time_distribution": [{"time_slot": "0-1h", "sessions": 1}], "estimated_sessions": 1})
+    db_session.add(ev)
+    db_session.commit()
+    s = IntelligentEvalSessionDB(eval_id=ev.id, target_id=ev.target_id, status="completed", goal="g")
+    db_session.add(s)
+    db_session.commit()
+
+    # 已有 worker 正常上报的两条日志
+    create_decision_log(ev.id, "execute_session", "时段0-1h欠账1个会话，需要执行会话", "manual-run-1", {}, db_session)
+    create_decision_log(ev.id, "start_analysis", "所有会话已完成，开始分析", "manual-run-1", {}, db_session)
+
+    assert supplement_decision_logs(db_session) == 0
+
+
+def test_fire_and_forget_does_not_block_and_logs_errors(monkeypatch):
+    """ADR-0011：触发派生 asyncio task 后台执行，异常在完成回调记录而不抛出。"""
+    import agenteval.intelligent_eval.scheduler as scheduler_mod
+
+    async def boom():
+        await asyncio.sleep(0)
+        raise RuntimeError("触发爆炸")
+
+    async def main():
+        scheduler_mod._fire_and_forget(boom(), "Worker")
+        await asyncio.sleep(0.01)  # 让被派生的任务及其完成回调执行
+        return "loop-continues"
+
+    # 直接挂 handler 到 agenteval logger：不依赖 caplog（全量跑时 alembic 迁移
+    # 用例的 fileConfig 会 disable_existing_loggers，把 agenteval logger 关掉）
+    records: list[logging.LogRecord] = []
+    handler = logging.Handler()
+    handler.emit = records.append
+    logger = logging.getLogger("agenteval")
+    was_disabled = logger.disabled
+    logger.disabled = False
+    logger.addHandler(handler)
+    try:
+        assert asyncio.run(main()) == "loop-continues"
+    finally:
+        logger.removeHandler(handler)
+        logger.disabled = was_disabled
+    assert any("触发爆炸" in r.getMessage() for r in records)