diff --git a/config.py b/config.py
index 1d2170d..52e04cd 100644
--- a/config.py
+++ b/config.py
@@ -325,7 +325,7 @@ YOUTUBE_API_KEY = os.getenv('YOUTUBE_API_KEY', '')
 # ==========================================
 # 系統版本與路徑
 # ==========================================
-SYSTEM_VERSION = "V10.422"
+SYSTEM_VERSION = "V10.423"
 LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log')
 public_url = PUBLIC_URL  # 用於模板顯示
 
diff --git a/docs/AI_INTELLIGENCE_MODULE_SOT.md b/docs/AI_INTELLIGENCE_MODULE_SOT.md
index a19c71c..2e35454 100644
--- a/docs/AI_INTELLIGENCE_MODULE_SOT.md
+++ b/docs/AI_INTELLIGENCE_MODULE_SOT.md
@@ -2,7 +2,7 @@
 
 > **最後更新**: 2026-05-24 (台北時間)
 > **狀態**: 🟢 四 AI Agent 自動化閉環已落地；LLM 路由紅線升級為 Ollama-first 三主機級聯，Gemini 備援預設關閉
-> **適用版本**: V10.422
+> **適用版本**: V10.423
 
 ---
 
@@ -34,6 +34,15 @@
 - 111 的 LAN 入口必須經 `scripts/ops/ollama111_allow_proxy.py` allowlist proxy：真實 Ollama 綁 `127.0.0.1:11434`，proxy 綁 `192.168.0.111:11434`，預設只允許 111 本機與 188 生產宿主；110 / 121 / 其他 LAN client 不能直接打 111，避免跨專案 CI 或 VM 繞過 momo-pro router 載入 7B+ runner。111 上以 `scripts/ops/install_ollama111_allow_proxy.sh` 安裝 user LaunchAgent，讓 proxy 與 `OLLAMA_HOST=127.0.0.1:11434` 在登入/重啟後自動恢復。
 - ElephantAlpha 的 `price_drop_alert` / `market_opportunity` Telegram HITL 告警必須把同款證據獨立呈現，至少包含 `match_type`、`price_basis`、`alert_tier` 與 `match_score`；沒有高信心同款與總價可比證據時，不得把 PChome/MOMO 價差寫成可直接跟價建議。
 
+## 零之一、12 Agent 決策信封（2026-05-24）
+
+- 12 角色分工不作為 12 個常駐模型；在產品層統一收斂成 `decision_envelope`，由 Hermes / NemoTron / OpenClaw / ElephantAlpha 與人工審核、PPT QA、競品 review queue 共用。
+- `decision_envelope` 必須至少能表達：`decision_type`、`severity`、`evidence[]`、`recommended_action`、`expected_impact`、`confidence`、`guardrails`、`trace`。
+- `guardrails.can_auto_execute=false` 是預設；價格調整、正式比價覆寫、PPT 發送與修復執行都必須遵守 HITL 或既有 service guard，不得因 Agent 信心高就繞過 matcher / feeder / review service。
+- 證據不足時不得輸出空泛效益預測；必須標記 `data_quality=missing|partial|stale`，並把建議行動降級成 `human_review`、`needs_research` 或 `silence_alert`。
+- Telegram `triaged_alert()` 已支援渲染 `decision_envelope`，讓告警固定呈現嚴重度、證據、建議行動、預期影響、信心度與追蹤 ID；後續觀測台與 PPT 也應共用同一份欄位語意。
+- 競品比價相關的 Agent 建議只能讀 `competitor_match_attempts` / review queue / `competitor_prices` 的既有證據；不得直接寫 `competitor_prices` 或覆蓋 `_should_upsert_competitor_price()` 的保護規則。
+
 ## 一、四 AI Agent 路由架構
 
 ```
diff --git a/docs/memory/history_logs.md b/docs/memory/history_logs.md
index 8dcfc7f..70f8315 100644
--- a/docs/memory/history_logs.md
+++ b/docs/memory/history_logs.md
@@ -13,6 +13,7 @@
 ## 📅 詳細更新日誌 (考古存檔)
 
 ### 2026-05-24：PChome 近門檻身份回收第二輪
+- **V10.423 12 Agent 決策信封**: `triaged_alert()` 支援 `decision_envelope` 結構化區塊，讓 Hermes / NemoTron / OpenClaw / ElephantAlpha 與後續 12 角色決策統一輸出 `severity`、`evidence`、`recommended_action`、`expected_impact`、`confidence`、`guardrails` 與 `trace`；缺證據時必須明確標記資料品質與 HITL 邊界，避免再出現空泛效益預測或不可追溯告警。
 - **V10.422 111 proxy LaunchAgent 持久化**: 新增 `scripts/ops/install_ollama111_allow_proxy.sh`，在 111 以 user LaunchAgent 安裝 `com.momo.ollama111-allow-proxy`，啟動時設定 `OLLAMA_HOST=127.0.0.1:11434`、重啟 Ollama、載入 allowlist proxy，避免重開機或重新登入後 111 又回到 LAN 全開狀態。
 - **V10.421 Kanebo Milano / hoi 蠟燭品類防錯配**: marketplace matcher 追加 `kanebo_milano_type_conflict` 與 `hoi_candle_line_conflict`，將 Kanebo Milano Collection 蜜粉餅 vs 絕色香水、hoi 日京山風香氛蠟燭 vs hoi!LAB 實驗室香氛蠟燭經典篇列為 hard veto；同品牌、同系列字樣或同容量仍不可跨品類/跨產品線直接比價。
 - **V10.420 111 Ollama LAN allowlist proxy**: 追查 111 高負載時確認來源不是 momo-pro，而是 110 上 `awoooi-cd` 臨時測試與 121 VMware VM 直接打 `192.168.0.111:11434`，繞過 `ai_calls` 與 momo-pro router 載入 7B runner。新增 `scripts/ops/ollama111_allow_proxy.py`，將真實 Ollama 收斂到 `127.0.0.1:11434`，由 user-space proxy 綁 `192.168.0.111:11434` 並預設只允許 111 本機與 188 生產宿主；110 / 121 會被 reset，111 fallback 保留給 momo production。
diff --git a/services/telegram_templates.py b/services/telegram_templates.py
index b76cd91..7783a00 100644
--- a/services/telegram_templates.py
+++ b/services/telegram_templates.py
@@ -696,6 +696,90 @@ def _format_ea_escalation_alert(
 
     return "\n".join(lines)
 
+
+def _format_decision_envelope(envelope: Dict[str, Any]) -> List[str]:
+    """將 12 Agent 共用決策信封轉成可審核的 Telegram 區塊。"""
+    if not isinstance(envelope, dict) or not envelope:
+        return []
+
+    severity = escape(str(envelope.get("severity") or "info"))
+    decision_type = escape(str(envelope.get("decision_type") or "general"))
+    confidence = envelope.get("confidence")
+    guardrails = envelope.get("guardrails") if isinstance(envelope.get("guardrails"), dict) else {}
+    data_quality = escape(str(guardrails.get("data_quality") or envelope.get("data_quality") or "unknown"))
+    can_auto_execute = bool(guardrails.get("can_auto_execute", False))
+    blocked_reason = escape(str(guardrails.get("blocked_reason") or ""))
+
+    confidence_text = ""
+    try:
+        if confidence is not None:
+            confidence_text = f"　信心度：<b>{float(confidence):.0%}</b>"
+    except (TypeError, ValueError):
+        confidence_text = ""
+
+    lines = [
+        "🧭 <b>決策信封</b>",
+        f"• 類型：<code>{decision_type}</code>　嚴重度：<b>{severity}</b>{confidence_text}",
+        f"• 資料品質：<code>{data_quality}</code>　自動執行：<b>{'允許' if can_auto_execute else '不允許'}</b>",
+    ]
+    if blocked_reason:
+        lines.append(f"• 邊界：{blocked_reason}")
+
+    evidence_items = envelope.get("evidence") if isinstance(envelope.get("evidence"), list) else []
+    if evidence_items:
+        lines += ["", "<b>證據</b>"]
+        for item in evidence_items[:3]:
+            if not isinstance(item, dict):
+                lines.append(f"• {escape(str(item))[:180]}")
+                continue
+            metric = escape(str(item.get("metric") or item.get("type") or "evidence"))
+            value = escape(str(item.get("value") if item.get("value") is not None else ""))
+            basis = escape(str(item.get("basis") or ""))
+            freshness = escape(str(item.get("freshness") or ""))
+            item_confidence = item.get("confidence")
+            confidence_suffix = ""
+            try:
+                if item_confidence is not None:
+                    confidence_suffix = f" / {float(item_confidence):.0%}"
+            except (TypeError, ValueError):
+                confidence_suffix = ""
+            detail = " / ".join(part for part in (value, basis, freshness) if part)
+            lines.append(f"• <code>{metric}</code>{confidence_suffix}" + (f"：{detail}" if detail else ""))
+
+    recommended_action = envelope.get("recommended_action")
+    if isinstance(recommended_action, dict):
+        action = escape(str(recommended_action.get("action") or "human_review"))
+        owner = escape(str(recommended_action.get("owner") or "未指定"))
+        deadline = escape(str(recommended_action.get("deadline") or ""))
+        requires_hitl = bool(recommended_action.get("requires_hitl", True))
+        lines += [
+            "",
+            "<b>建議行動</b>",
+            f"• 動作：<code>{action}</code>　負責：<b>{owner}</b>",
+            f"• HITL：<b>{'需要' if requires_hitl else '不需要'}</b>" + (f"　期限：{deadline}" if deadline else ""),
+        ]
+
+    expected_impact = envelope.get("expected_impact")
+    if isinstance(expected_impact, dict) and expected_impact:
+        impact_parts = []
+        for key in ("revenue_loss_7d", "gap_amount", "cost_usd", "risk_reduction"):
+            if key in expected_impact and expected_impact[key] is not None:
+                impact_parts.append(f"{escape(key)}={escape(str(expected_impact[key]))}")
+        if impact_parts:
+            lines += ["", "<b>預期影響</b>", "• " + " / ".join(impact_parts[:4])]
+
+    trace = envelope.get("trace")
+    if isinstance(trace, dict):
+        trace_parts = []
+        for key in ("ai_call_id", "insight_id", "action_plan_id", "model", "provider"):
+            if trace.get(key) is not None:
+                trace_parts.append(f"{key}={trace[key]}")
+        if trace_parts:
+            lines += ["", f"<code>{escape(' | '.join(trace_parts))}</code>"]
+
+    return lines + [""]
+
+
 def triaged_alert(base_event: Dict[str, Any], tier_label: str,
                   ai_summary: str, ai_cause: Optional[str] = None,
                   ai_actions: Optional[list] = None,
@@ -730,6 +814,9 @@ def triaged_alert(base_event: Dict[str, Any], tier_label: str,
             lines += [f"🧠 <b>AI 摘要：</b>{safe_ai_summary[:400]}", ""]
         if safe_ai_cause:
             lines += [f"💡 <b>可能原因：</b>{safe_ai_cause}", ""]
+        decision_envelope = base_event.get("decision_envelope") or base_event.get("decision")
+        if isinstance(decision_envelope, dict):
+            lines += _format_decision_envelope(decision_envelope)
         if safe_actions:
             lines += ["<b>📋 建議行動：</b>"] + [f"  • {a}" for a in safe_actions] + [""]
         if safe_executed:
diff --git a/tests/test_telegram_triaged_alert_format.py b/tests/test_telegram_triaged_alert_format.py
index dc55eca..9fac9aa 100644
--- a/tests/test_telegram_triaged_alert_format.py
+++ b/tests/test_telegram_triaged_alert_format.py
@@ -124,3 +124,73 @@ def test_ea_escalation_generic_actions_do_not_render_as_sku_cards():
     assert "📋 <b>TOP 待審 SKU</b>" not in msg
     assert "• 待審 SKU" not in msg
     assert "未取得實證前，不執行自動調價、修復或策略派發" in msg
+
+
+def test_triaged_alert_renders_decision_envelope_contract():
+    msg, keyboard = triaged_alert(
+        base_event={
+            "event_type": "price_alert",
+            "title": "MOMO / PChome 價格威脅",
+            "summary": "高信心同款且 PChome 低價。",
+            "id": "decision_env_001",
+            "decision_envelope": {
+                "decision_type": "price_alert",
+                "severity": "P1",
+                "confidence": 0.86,
+                "evidence": [
+                    {
+                        "type": "match",
+                        "metric": "match_score",
+                        "value": 0.91,
+                        "basis": "identity_v2 + price_alert_exact",
+                        "freshness": "2026-05-24T10:00:00+08:00",
+                        "confidence": 0.91,
+                    },
+                    {
+                        "type": "price",
+                        "metric": "gap_pct",
+                        "value": "18.4%",
+                        "basis": "latest price_records",
+                    },
+                ],
+                "recommended_action": {
+                    "action": "human_review",
+                    "owner": "ops",
+                    "deadline": "2026-05-24T18:00:00+08:00",
+                    "requires_hitl": True,
+                },
+                "expected_impact": {
+                    "revenue_loss_7d": 42000,
+                    "gap_amount": 120,
+                    "risk_reduction": "high",
+                },
+                "guardrails": {
+                    "can_auto_execute": False,
+                    "blocked_reason": "price adjustment requires HITL",
+                    "data_quality": "complete",
+                },
+                "trace": {
+                    "ai_call_id": 123,
+                    "action_plan_id": 456,
+                    "model": "qwen3:14b",
+                    "provider": "ollama_gcp_a",
+                },
+            },
+        },
+        tier_label="Hermes · P1",
+        ai_summary="建議進人工價格審核。",
+    )
+
+    assert "🧭 <b>決策信封</b>" in msg
+    assert "類型：<code>price_alert</code>" in msg
+    assert "嚴重度：<b>P1</b>" in msg
+    assert "信心度：<b>86%</b>" in msg
+    assert "資料品質：<code>complete</code>" in msg
+    assert "自動執行：<b>不允許</b>" in msg
+    assert "邊界：price adjustment requires HITL" in msg
+    assert "<code>match_score</code> / 91%" in msg
+    assert "identity_v2 + price_alert_exact" in msg
+    assert "動作：<code>human_review</code>" in msg
+    assert "revenue_loss_7d=42000" in msg
+    assert "ai_call_id=123" in msg
+    assert keyboard["inline_keyboard"][0][0]["callback_data"] == "momo:eig:decision_env_001"