diff --git a/apps/api/src/jobs/hermes_rule_quality_job.py b/apps/api/src/jobs/hermes_rule_quality_job.py
new file mode 100644
index 00000000..cb35c2f9
--- /dev/null
+++ b/apps/api/src/jobs/hermes_rule_quality_job.py
@@ -0,0 +1,254 @@
+"""
+Hermes Rule Quality Advisor — ADR-090 § E3 AI 規則品質建議
+==========================================================
+每日 04:00 Taipei 分析 alert_rule_catalog,對 noise_rate > 0.7 的 rule 推 Telegram
+建議 + 寫 aol(rule_rejected) 稽核,人工決策是否 deprecate.
+
+職責邊界:
+  ✅ 讀 alert_rule_catalog WHERE noise_rate >= 0.7
+  ✅ 為每條寫 aol(rule_rejected) + proposed_action='review_or_deprecate'
+  ✅ 推 Telegram 通知 SRE group (格式化清單)
+  ⏳ 不自動改 review_status (統帥鐵律: AI 不做最終決策)
+  ⏳ TODO: LLM 分析每條 rule 的假報真因 (下一階段)
+
+統帥鐵律對齊:
+  - 禁止寫死規則做最終決策 → 本 agent 只推建議,人工決策
+  - 朝 AI 自主化方向 → aol 留 trail,未來可升級為 LLM 判斷
+  - noise_rate threshold 0.7 是「觸發討論」而非「自動動作」
+
+排程:
+  - 首次延遲 420s
+  - 每日 04:00 Taipei
+
+2026-04-19 ogt + Claude Opus 4.7 (1M context) Asia/Taipei
+ADR-090 § E3 Hermes
+"""
+from __future__ import annotations
+
+import asyncio
+import json as _json
+import time as _time
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+import structlog
+
+logger = structlog.get_logger(__name__)
+
+_FIRST_DELAY_SEC = 420
+_LOOP_BACKOFF_SEC = 1800
+_DAILY_TRIGGER_HOUR_TAIPEI = 4
+
+# 觸發討論的噪音閾值
+_NOISE_THRESHOLD = 0.7
+# 樣本不足不發建議 (避免只 fire 1 次就標為噪音)
+_MIN_SAMPLE_SIZE = 5
+
+
+async def run_hermes_rule_quality_loop() -> None:
+    """每日 04:00 分析 rule 品質."""
+    logger.info("hermes_rule_quality_loop_started")
+    await asyncio.sleep(_FIRST_DELAY_SEC)
+
+    while True:
+        try:
+            await analyze_once()
+        except Exception as e:
+            logger.exception("hermes_rule_quality_loop_error", error=str(e))
+            await asyncio.sleep(_LOOP_BACKOFF_SEC)
+            continue
+
+        sleep_sec = _seconds_until_next_trigger()
+        logger.info("hermes_rule_quality_next_tick", sleep_sec=sleep_sec)
+        await asyncio.sleep(sleep_sec)
+
+
+async def analyze_once() -> dict[str, int]:
+    """一次分析: 找噪音 rule + 推建議."""
+    started_ms = _time.time()
+    stats = {"noisy_rules": 0, "advisories_written": 0, "telegram_sent": 0}
+    error_msg: str | None = None
+
+    try:
+        noisy = await _fetch_noisy_rules()
+        stats["noisy_rules"] = len(noisy)
+
+        for r in noisy:
+            ok = await _write_advisory_aol(r)
+            if ok:
+                stats["advisories_written"] += 1
+
+        if noisy:
+            sent = await _send_telegram_summary(noisy)
+            stats["telegram_sent"] = 1 if sent else 0
+
+    except Exception as e:
+        error_msg = f"{type(e).__name__}: {e}"[:1000]
+        logger.exception("hermes_analyze_once_failed", error=error_msg)
+
+    duration_ms = int((_time.time() - started_ms) * 1000)
+    logger.info(
+        "hermes_rule_quality_once_done",
+        noisy=stats["noisy_rules"],
+        advisories=stats["advisories_written"],
+        telegram_sent=stats["telegram_sent"],
+        duration_ms=duration_ms,
+    )
+    return stats
+
+
+# ============================================================================
+# 資料查詢
+# ============================================================================
+
+async def _fetch_noisy_rules() -> list[dict[str, Any]]:
+    """撈 noise_rate >= 0.7 且樣本 >= 5 的 rules."""
+    from sqlalchemy import text as _sql
+    from src.db.base import get_db_context
+
+    try:
+        async with get_db_context() as db:
+            result = await db.execute(
+                _sql(f"""
+                    SELECT
+                        rule_id, rule_name, severity,
+                        true_positive_count, false_positive_count, noise_rate,
+                        last_fired_at, review_status
+                    FROM alert_rule_catalog
+                    WHERE noise_rate >= :thr
+                      AND (true_positive_count + false_positive_count) >= :min_sample
+                      AND (review_status IS NULL OR review_status = 'approved')
+                    ORDER BY noise_rate DESC, (true_positive_count + false_positive_count) DESC
+                """),
+                {"thr": _NOISE_THRESHOLD, "min_sample": _MIN_SAMPLE_SIZE},
+            )
+            return [
+                {
+                    "rule_id": r.rule_id,
+                    "rule_name": r.rule_name,
+                    "severity": r.severity,
+                    "tp": int(r.true_positive_count or 0),
+                    "fp": int(r.false_positive_count or 0),
+                    "noise_rate": float(r.noise_rate) if r.noise_rate else 0.0,
+                    "last_fired_at": r.last_fired_at,
+                    "review_status": r.review_status,
+                }
+                for r in result.fetchall()
+            ]
+    except Exception as e:
+        logger.warning("fetch_noisy_rules_failed", error=str(e))
+        return []
+
+
+# ============================================================================
+# 建議寫入 (aol only,不改 rule 本身)
+# ============================================================================
+
+async def _write_advisory_aol(rule: dict[str, Any]) -> bool:
+    """寫 aol(rule_rejected) — 紀錄 AI 建議人工審查."""
+    try:
+        from sqlalchemy import text as _sql
+        from src.db.base import get_db_context
+
+        input_payload = {
+            "rule_name": rule["rule_name"],
+            "severity": rule["severity"],
+            "noise_rate": rule["noise_rate"],
+            "true_positive_count": rule["tp"],
+            "false_positive_count": rule["fp"],
+        }
+        output_payload = {
+            "proposed_action": "review_or_deprecate",
+            "reason": (
+                f"過去 30d noise_rate {rule['noise_rate']:.1%} "
+                f"(tp={rule['tp']}, fp={rule['fp']}),"
+                f"假報過多應考慮 deprecate 或改進 expr"
+            ),
+            "requires_human_decision": True,
+        }
+
+        async with get_db_context() as db:
+            await db.execute(
+                _sql("""
+                    INSERT INTO automation_operation_log (
+                        operation_type, actor, status,
+                        input, output, tags
+                    ) VALUES (
+                        'rule_rejected',
+                        'hermes_rule_quality',
+                        'success',
+                        CAST(:input AS jsonb),
+                        CAST(:output AS jsonb),
+                        :tags
+                    )
+                """),
+                {
+                    "input": _json.dumps(input_payload, ensure_ascii=False),
+                    "output": _json.dumps(output_payload, ensure_ascii=False),
+                    "tags": ["hermes", "rule_quality", "advisory"],
+                },
+            )
+        return True
+    except Exception as e:
+        logger.warning("write_advisory_aol_failed", rule=rule["rule_name"], error=str(e))
+        return False
+
+
+# ============================================================================
+# Telegram 推送
+# ============================================================================
+
+async def _send_telegram_summary(noisy: list[dict[str, Any]]) -> bool:
+    """推 Telegram 摘要訊息給 SRE group."""
+    try:
+        from src.core.config import settings
+        from src.services.telegram_gateway import get_telegram_gateway
+
+        if not settings.OPENCLAW_TG_CHAT_ID:
+            logger.info("hermes_telegram_skip_no_chat_id")
+            return False
+
+        lines = [
+            f"🔍 <b>Hermes 規則品質檢測</b>",
+            f"檢測到 {len(noisy)} 條規則噪音率 ≥ {_NOISE_THRESHOLD:.0%},建議人工審查:",
+            "",
+        ]
+        for r in noisy[:10]:  # 最多秀 10 條避免太長
+            import html
+            safe_name = html.escape(r["rule_name"])
+            lines.append(
+                f"🟡 <code>{safe_name}</code>\n"
+                f"   噪音率 <b>{r['noise_rate']:.1%}</b> (tp={r['tp']} fp={r['fp']} sev={r['severity'] or '-'})"
+            )
+        if len(noisy) > 10:
+            lines.append(f"\n…還有 {len(noisy) - 10} 條")
+        lines.append("\n人工決策: 確認 deprecate 或改 expr → 手動 UPDATE review_status")
+
+        msg = "\n".join(lines)
+
+        tg = get_telegram_gateway()
+        # 直接用 telegram_gateway._send_request 送一般訊息
+        await tg._send_request("sendMessage", {  # type: ignore[attr-defined]
+            "chat_id": settings.OPENCLAW_TG_CHAT_ID,
+            "text": msg,
+            "parse_mode": "HTML",
+            "disable_web_page_preview": True,
+        })
+        return True
+    except Exception as e:
+        logger.warning("hermes_telegram_send_failed", error=str(e))
+        return False
+
+
+# ============================================================================
+# 時間
+# ============================================================================
+
+def _seconds_until_next_trigger() -> float:
+    tz_taipei = timezone(timedelta(hours=8))
+    now = datetime.now(tz_taipei)
+    today_trigger = now.replace(hour=_DAILY_TRIGGER_HOUR_TAIPEI, minute=0, second=0, microsecond=0)
+    if now >= today_trigger:
+        today_trigger = today_trigger + timedelta(days=1)
+    delta = (today_trigger - now).total_seconds()
+    return max(300.0, min(delta, 25 * 3600))
diff --git a/apps/api/src/main.py b/apps/api/src/main.py
index 1bb609e5..9405c7ee 100644
--- a/apps/api/src/main.py
+++ b/apps/api/src/main.py
@@ -440,6 +440,16 @@ async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]:
     except Exception as e:
         logger.warning("asset_change_tracker_loop_schedule_failed", error=str(e))
 
+    # ADR-090 § Hermes Rule Quality Advisor (2026-04-19 ogt + Claude Opus 4.7 Asia/Taipei)
+    # 每日 04:00 Taipei 分析 alert_rule_catalog.noise_rate,對高噪音規則推 Telegram 建議
+    # 統帥鐵律: AI 只推建議不自動改 review_status,人工決策 deprecate
+    try:
+        from src.jobs.hermes_rule_quality_job import run_hermes_rule_quality_loop
+        asyncio.create_task(run_hermes_rule_quality_loop())
+        logger.info("hermes_rule_quality_loop_scheduled", daily_trigger_hour_taipei=4)
+    except Exception as e:
+        logger.warning("hermes_rule_quality_loop_schedule_failed", error=str(e))
+
     # ADR-076 Task 4: 每日 08:00 台北時間自動日度巡檢報告
     # 2026-04-14 Claude Haiku 4.5 Asia/Taipei
     try: