OG T
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ceb61c3c8e
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feat(asset_scanner): Gap 1 修 — Prometheus targets 補齊 host-install services
CD Pipeline / build-and-deploy (push) Successful in 13m32s
Audit 發現 asset_inventory 只涵蓋 K8s (mon=120, mon1=121 共 2 node+78 pods),
完全漏 110 (Harbor/Gitea/監控) + 112 (security) + 188 (PG/Redis/Ollama) +
125 (mon backup/standby) 這 4 主機的 host-install services.
用戶 4 主機架構 (110/112/120/121/188) 只覆蓋 2/5 = 40%.
新增 _collect_prometheus_targets:
GET /api/v1/targets?state=active → 自動發現全部被監控的:
- host_service (IP 形式 target → postgres-110/redis-110/minio-188/node-exporter 等)
- third_party_service (非 IP 如 alertmanager/argocd-server)
- host (每個 unique IP 建 asset_type='host')
- target → host 的 depends_on relationship
預期新增 asset_inventory:
- host: 6 個 (110/112/120/121/125/188,Prometheus 看到的 blackbox-icmp 全覆蓋)
- host_service: ~15 個 (postgres/redis/minio/node-exporter/cadvisor 等)
- third_party_service: ~5 個 (alertmanager/argocd/prometheus/velero 等)
解鎖:
- 110/112/188 host-install services 進入 asset_inventory
- coverage_evaluator 可評估這些 asset (monitoring/alerting/playbook 等 7 維)
- blast_radius_calculator 可查「110 PostgreSQL 影響哪些 service」
- Hermes/forecaster 建議範圍擴大到非 K8s 服務
對齊統帥鐵律: 朝 AI 自主化 — 不硬編主機清單,動態從 Prometheus 發現
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 20:06:34 +08:00 |
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OG T
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a391dfc389
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feat(aiops): capacity_forecaster — Phase 4 Holt-Winters MVP (predict_linear)
CD Pipeline / build-and-deploy (push) Has been cancelled
統帥批准 4 項下階段候選之一完成: AI 容量預測.
新增 capacity_forecaster_job.py (~220 行):
每日 05:00 Taipei 跑預測 (02:00 scanner → 03:00 compliance →
04:00 Hermes → 05:00 forecaster 形成完整日鏈).
預測方法論 (MVP):
Prometheus predict_linear(metric[7d], 86400*7) — 基於過去 7d 做線性外推
3 個預測 query:
1. disk_saturation_7d: predict_linear(node_filesystem_avail_bytes[7d], 7d) < 0
2. mem_saturation_7d: predict_linear(MemAvailable[7d], 7d) / MemTotal < 10%
3. cpu_high_7d_trend: avg_over_time(cpu_used_pct[7d]) > 70%
發現高風險 host → 寫 aol(capacity_recommendation) + 推 Telegram
- input: host + horizon + findings count
- output: findings list + proposed_actions + requires_human_decision=true
proposed_actions 依 findings 推導:
- disk: 清理 log/docker/PG WAL 或擴容
- mem: top consumer / JVM 調整
- cpu: scale out / vCPU 擴充
統帥鐵律對齊:
✅ 只推建議不自動 scale up
✅ 7d window 有足夠樣本
✅ AI 預測 + 人工決策
未來 TODO:
- 真 Holt-Winters (含季節性) — 需 Python statsmodels
- 業務週期調整 (週一高峰/週末低谷)
Wire main.py lifespan asyncio.create_task()
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 20:00:36 +08:00 |
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OG T
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c1f23cfabe
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feat(coverage_evaluator): 擴充 4 維 — playbook/remediation/rule_matching/rule_creation
CD Pipeline / build-and-deploy (push) Has been cancelled
Review 盲點: coverage 7 維中原只實作 3 維 (monitoring/alerting/km),其餘 4 維永遠 unknown
v2 擴充:
+ auto_playbook: asset.name 出現在 playbooks.symptom_pattern/description (approved 狀態) → green
沒對應 playbook 但 type='k8s_workload' → yellow
+ auto_remediation: 過去 30d remediation_events.target_resource ILIKE asset.name → green
沒 target 但 k8s_workload/container → red (應有修復能力但沒)
+ auto_rule_matching: 過去 30d incidents.affected_services ILIKE asset.name
或 incidents.alertname match alert_rule.labels.host/namespace → green
沒觸發 → yellow (可能沒問題也可能沒覆蓋)
+ auto_rule_creation: alert_rule_catalog source='ai_generated' match asset → green
目前全 yaml_hardcoded → 全 red (表示尚未由 AI 主動建規則)
未來 Hermes 產出 AI rule 後會變 green
解鎖: coverage 7 維完整 SLO KPI (MASTER §7.1)
- red count = 真正的治理缺口
- green ratio = 自動化成熟度
- AI 可主動推薦 red asset 的補覆蓋動作
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 19:54:36 +08:00 |
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OG T
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ba18ad2ef8
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feat(hermes+rules): LLM 升級 Hermes + 統帥決策 deprecate PostgreSQLDiskGrowthRate
Deploy Alert Rules / Deploy Prometheus Alert Rules (push) Successful in 40s
CD Pipeline / build-and-deploy (push) Successful in 8m37s
統帥 2026-04-19 決策:
- Rule 1 PostgreSQLDiskGrowthRate → 選項 C: deprecate + 替代新規則
- Rule 2 NoAlertsReceived2Hours → 保留 (真實告警鏈路守護)
- noise_rate 算法先修正 (NO_ACTION 不算 fp),觀察後動態調整
1. rule_stats_updater v2 noise 算法:
原: 任何 EXPIRED approval 都算 fp
問題: NO_ACTION/OBSERVE/INVESTIGATE 是 AI 純觀察,不該算假報
修: WHERE ar.action NOT ILIKE '%NO_ACTION%' AND NOT ILIKE '%OBSERVE%' AND ...
2. hermes_rule_quality v2 LLM 升級:
新增 _llm_analyze_noisy_rule:
- 用 OpenClaw (Ollama/NemoTron/Gemini) 分析每條噪音 rule
- JSON 輸出: probable_root_causes/recommended_actions/confidence/should_deprecate
- 3 路 parse fallback (直接 / NemoTron wrapper / description nested)
_write_advisory_aol 加 llm_analysis 到 output_payload
_send_telegram_summary 加 AI 判定 + top 2 建議 (8 條上限避免太長)
符合統帥鐵律: AI 分析但不自動動作,仍人工決策
3. ops/monitoring/alerts-unified.yml 替換 Rule 1:
刪 PostgreSQLDiskGrowthRate (500MB/h 增長 → 觸發 WAL 正常行為誤報)
加 HostDiskUsageHigh (>80% for 10m, warning)
加 HostDiskUsageCritical (>90% for 5m, critical)
兩者 labels.supersedes='PostgreSQLDiskGrowthRate' 供追溯
(待 deploy-alerts workflow 下次 apply 到 Prometheus)
4. DB 即時 mark deprecated (避免等 alerts yaml 部署前 Hermes 又推):
UPDATE alert_rule_catalog SET review_status='deprecated' WHERE rule_name='PostgreSQLDiskGrowthRate'
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 19:39:05 +08:00 |
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OG T
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6ab0ce9c75
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feat(aiops): Hermes rule quality advisor — E3 AI 規則品質建議 (保守版)
CD Pipeline / build-and-deploy (push) Successful in 8m22s
實證 rule_stats 跑完後發現 2 條 100% noise_rate 規則:
- PostgreSQLDiskGrowthRate (tp=0 fp=2)
- NoAlertsReceived2Hours (tp=0 fp=1)
加上 MoWoooWorkDown (33%), KubePodCrashLooping (25%)
新增 hermes_rule_quality_job.py (~210 行):
每日 04:00 Taipei 分析 alert_rule_catalog:
- threshold: noise_rate >= 0.7 AND 樣本 >= 5
- 為每條寫 aol('rule_rejected', proposed_action='review_or_deprecate')
- 推 Telegram 摘要給 SRE group
統帥鐵律對齊:
✅ 不自動改 review_status (人工決策 deprecate,AI 只推建議)
✅ threshold 作為「觸發討論」而非「最終決策」
✅ aol(rule_rejected) 留 trail,未來可升級 LLM 辯證
解鎖 E3 Hermes 基礎: 後續可加 LLM 分析假報真因 (expr 缺 for: window、
label match 太寬泛、metric 本身 noisy 等),產出具體改進建議.
Wire main.py lifespan asyncio.create_task()
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 18:11:26 +08:00 |
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OG T
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e677773e39
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fix(asset_scanner): Pod→Deployment via ReplicaSet 橋樑 (relationship 漏掉修復)
CD Pipeline / build-and-deploy (push) Successful in 9m31s
Review 盲點: 實測 asset_relationship 52 筆,但都是 Pod→StatefulSet + Pod→ConfigMap,
完全沒有 Pod→Deployment!
真因:
K8s 中 Pod.ownerReferences[0].kind = 'ReplicaSet' (99% 案例)
Deployment 管 ReplicaSet 管 Pod (兩層 owner chain)
原 code 只 match kind in (deployment/statefulset/daemonset) → 跳過 ReplicaSet
→ Pod→Deployment 關係全部漏掉
修復 v3.1:
0. 新增 collect replicasets pass (僅作為 bridge,不寫 asset_inventory)
建 rs_to_deployment map: {ns/rs_name: deployment_name}
2. Pod ownerRef.kind='ReplicaSet' → 反查 rs_to_deployment → 建 Pod→Deployment
預期效果:
- asset_relationship 從 52 → 150+ (所有 Deployment-managed Pod 都有 relationship)
- OpenClaw blast_radius 計算 Deployment 影響的 Pod 數 = 正確
不寫 ReplicaSet 為 asset (他是 ephemeral 中介,滾動更新會大量產生,污染 inventory)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 17:26:57 +08:00 |
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OG T
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c8b263db06
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fix(coverage_evaluator): KM 欄位修正 ke.body → ke.content + 擴大 title 匹配
CD Pipeline / build-and-deploy (push) Has been cancelled
實測 df71c9a 部署後 coverage_evaluator 生效:
- monitoring: 2 hosts match Prometheus targets
- alerting: 74 筆 (22 green + 52 red)
- km: 0 (錯誤: column "ke.body" does not exist)
真因: knowledge_entries 表欄位是 'content' 不是 'body'
修復: ke.content ILIKE '%name%' OR ke.title ILIKE '%name%'
同時清 unused import (typing.Any)
下輪 coverage_evaluator tick 將正確 UPDATE auto_km_creation 維度
解鎖完整 3 維 coverage (monitoring/alerting/km)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 17:24:46 +08:00 |
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OG T
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92349bc37c
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feat(aiops): asset_change_tracker — 8 張 0 writer 表全數上線
CD Pipeline / build-and-deploy (push) Has been cancelled
Review 盲點 10: asset_change_event 仍 0 筆 (最後一張 0 writer 表)
新增 asset_change_tracker_job.py (~180 行):
每 1h 比對最近兩次 asset_discovery_run,寫 asset_change_event:
✅ asset_added: newer run 有但 older run 沒有 (EXCEPT SET)
✅ asset_removed: older 有但 newer 沒有
✅ lifecycle_changed: asset_inventory.lifecycle_state='deprecated' 且 updated_at 近 2h
使用 SET EXCEPT 避免 N+1, 單次 INSERT 完成所有 diff
8 張 ADR-090 0 writer 表到此全數有 writer:
✅ asset_inventory / asset_discovery_run / asset_coverage_snapshot
/ asset_relationship / asset_change_event / asset_compliance_snapshot (asset_*)
✅ alert_rule_catalog
✅ host_capacity_snapshot / capacity_violation_event (capacity_*)
Phase 7 資產盤點 + 覆蓋矩陣 + 變化追蹤完整實作.
接下來可以上 Hermes AI agent 分析品質 (deprecate noisy rules, 推薦 coverage 修復).
Wire main.py lifespan asyncio.create_task()
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 17:18:34 +08:00 |
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OG T
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df71c9a37b
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feat(aiops): rule_stats_updater — 計算 noise_rate + true/false positive
CD Pipeline / build-and-deploy (push) Successful in 8m26s
Review 盲點 5: alert_rule_catalog 68 筆但 noise_rate/TP/FP/last_fired_at 全 NULL
新增 rule_stats_updater_job.py (~170 行):
每 1h UPDATE 全表 alert_rule_catalog,從 incidents + approval_records 推算:
- last_fired_at = max(incidents.created_at WHERE alertname=rule_name)
- true_positive_count = count incidents.status='RESOLVED' past 30d
- false_positive_count = count approval_records.status='EXPIRED' past 30d
(EXPIRED = 48h 無人處理,視為假警報 proxy)
- noise_rate = fp / (tp + fp)
窗口: 30 天 (可配置)
使用單一 UPDATE + subquery,避免 N+1 (68 rule × 3 query = 204 queries → 1 query)
解鎖 E3 Hermes:
後續 Hermes AI agent 讀 alert_rule_catalog WHERE noise_rate > 0.5
提案 review_status='deprecated' 或 superseded_by_rule_id
Wire main.py lifespan asyncio.create_task()
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 17:05:30 +08:00 |
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OG T
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505232336b
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feat(aiops): coverage_evaluator — 把 coverage_snapshot 從 unknown 升為真實 status
CD Pipeline / build-and-deploy (push) Has been cancelled
Review 盲點 4: asset_coverage_snapshot 546 筆全是 'unknown',沒實際意義
新增 coverage_evaluator_job.py (~270 行):
每 1h 針對最新 asset_discovery_run 的 coverage_snapshot 做 3 維升級:
✅ auto_monitoring: Prometheus /api/v1/targets 看 host asset IP
→ green (有 target) / red (無 target)
✅ auto_alerting: alert_rule_catalog.labels 是否 match asset
→ host/namespace/layer 三種 match 策略, green/red
✅ auto_km_creation: knowledge_entries.body ILIKE asset.name
→ green (有 KM) / yellow (無 KM)
evidence JSONB 記錄升級依據,供 AI 後續稽核
未實作 (留 unknown):
⏳ auto_rule_matching (需 alert history 統計)
⏳ auto_playbook / auto_remediation / auto_rule_creation (需 playbook 表)
預期效果 (下次 evaluator 跑 + coverage_snapshot UPDATE):
- 546 筆 coverage 從 100% unknown → 30-50% green/red/yellow
- 真正可以算 "覆蓋率 SLO" KPI (MASTER §7.1)
- AI 可從 coverage_snapshot 看出 red asset,主動推 remediation
Wire main.py lifespan asyncio.create_task()
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 17:02:30 +08:00 |
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OG T
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fdf8b739f1
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feat(asset_scanner): v3 擴充多資源類型 + asset_relationship builder
CD Pipeline / build-and-deploy (push) Has been cancelled
Review 原本 MVP 只掃 pods (39 assets) 盲點,本次擴充:
新增資源類型掃描:
- nodes (asset_type='host') — 實體主機
- deployments/statefulsets/daemonsets (asset_type='k8s_workload')
- services (asset_type='k8s_resource')
- configmaps (asset_type='k8s_resource')
跳過 secrets (awoooi-executor RBAC 禁止 list,正確設計)
新增 asset_relationship 自動建立:
- Pod → Deployment/StatefulSet/DaemonSet (depends_on, via ownerReferences)
- Service → Pod (routes_to, via spec.selector 匹配 Pod.labels)
- Pod → ConfigMap (depends_on, via spec.volumes[].configMap.name)
用 ON CONFLICT (from/to/type) DO UPDATE last_verified_at 保持 idempotent
新增 _fetch_kubectl_json helper (nodes 不帶 --all-namespaces)
新增 _build_{pod,workload,service,node,configmap}_asset 各自 asset 建構器
預期效果 (下次 scan 1h 後或 Pod 重啟時):
- asset_inventory: 39 → 300+ (全集群多種資源)
- asset_relationship: 0 → 數百 (OpenClaw 爆炸半徑計算終於有拓樸)
解鎖下游:
- AI 計算 blast_radius 可查 asset_relationship (之前無資料)
- MASTER §3.3 D3 Declarative Remediation 的 blast_radius_calculator 有真實依賴圖
Refs: ADR-090 §3.3, MASTER §3.1 L6×D1 (8D 感官拓樸)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 16:54:18 +08:00 |
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OG T
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02263445c2
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fix(asset_scanner): kubectl 改 subprocess — K8sProvider 不支援 --all-namespaces
CD Pipeline / build-and-deploy (push) Successful in 9m9s
5b9b36f 部署後 asset_scanner 跑 3 次但 total=0, new=0:
- asset_inventory 仍 0 筆
- Pod 手動 kubectl get pods --all-namespaces -o json 可取 JSON
- 真因: K8sProvider._kubectl_get 把 namespace 參數塞進 '-n $ns',
所以 '--all-namespaces' 變成 '-n --all-namespaces' (kubectl 拒絕)
修復:
- 不走 K8sProvider,直接 asyncio.create_subprocess_exec
- kubectl get pods --all-namespaces -o json
- 30s timeout,rc != 0 拋 RuntimeError 觸發 aol status='failed'
驗證: 部署後 asset_inventory 應在 1 分鐘內開始有 pods 寫入
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 16:31:26 +08:00 |
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OG T
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4259a104f5
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feat(aiops): capacity_scanner + compliance_scanner (ADR-090 Phase 7 剩 2)
CD Pipeline / build-and-deploy (push) Has been cancelled
完成 ADR-090 Phase 7 第 3+4 個 service,解鎖 2 張 0 writer 表:
B3. apps/api/src/jobs/capacity_scanner_job.py (~300 行)
- 每日 02:00 Taipei 撈 Prometheus node_exporter
- 寫 host_capacity_snapshot (load1/5/15, cpu, iowait, mem, swap)
- heuristic ai_verdict: cpu>80 or mem>85 → critical; >60/70 → warning
- 超過硬閾值 → 寫 capacity_violation_event
- 寫 aol(capacity_recommendation)
B4. apps/api/src/jobs/compliance_scanner_job.py (~270 行)
- 每日 03:00 Taipei 遍歷 asset_inventory active assets
- 為每個 asset 寫 7 維 compliance snapshot
- secret_rotated: 真實檢查 (metadata.creationTimestamp > 90d = warning)
- 其他 6 維 (ssl_cert_valid / cve_scan / backup_tested /
audit_log_enabled / access_reviewed / encryption_at_rest) 占位 'unknown'
+ detail TODO,後續 agent 補邏輯
- 寫 aol(coverage_recalculated) summary
main.py lifespan 同步 wire 2 個新 loop
預期解鎖 (配合 B1 asset_scanner + B2 rule_catalog_sync):
- asset_inventory: 0 → 數百 (B1)
- asset_discovery_run: 0 → 每小時 1 (B1)
- asset_coverage_snapshot: 0 → assets × 7 維 (B1)
- alert_rule_catalog: 0 → ~68 條 (B2)
- host_capacity_snapshot: 0 → 每日 hosts (B3)
- capacity_violation_event: 0 → 超閾值時 (B3)
- asset_compliance_snapshot: 0 → assets × 7 維 (B4)
automation_operation_log 新增 4 個 op_type: asset_discovered / rule_created /
rule_updated / capacity_recommendation / coverage_recalculated
8 張 0 writer 表到此全數有 writer,ADR-090 Phase 7 實作完成.
Refs: ADR-090 §4.2 Phase 4, MASTER §3.5 D5 (capacity-aware)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 16:23:27 +08:00 |
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OG T
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5b9b36f30d
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fix(ci)+feat(aiops): cd.yaml shared network + rule_catalog_sync (ADR-090 E3)
CD Pipeline / build-and-deploy (push) Successful in 14m31s
CI 修復 (c0f3509 第三次 fail 真因):
c0f3509 log: 'Detected act task network: (none, will fall back to bridge)'
→ grep ACT_NET 在 CI 環境未 match → fallback bridge
→ default bridge 不支援 container name DNS → pg-test-b5 解析失敗
修復 (v3 — 主動創 shared network):
- B5_NET=b5-test-net (idempotent docker network create)
- ci-runner 自己 docker network connect $HOSTNAME
- pg-test-b5 --network=$B5_NET
- 兩邊同 user-defined network → container name DNS 正常
新增 rule_catalog_sync_job (ADR-090 § Phase 7 第 2 個 service):
+ apps/api/src/jobs/rule_catalog_sync_job.py (~230 行)
- run_rule_catalog_sync_loop: 啟動延遲 90s,每 1h sync
- sync_once: HTTP GET {PROMETHEUS_URL}/api/v1/rules (type=alert)
- UPSERT alert_rule_catalog (rule_name 為 UNIQUE)
- 只在實際 INSERT/UPDATE 發生時才寫 aol (避免 N 條 rule 污染)
+ main.py lifespan asyncio.create_task() wire
預期解鎖:
- alert_rule_catalog: 從 0 → Prometheus active rules 數 (~68 條)
- automation_operation_log: 新增 'rule_created' / 'rule_updated' op_type
- E3 Hermes AI 終於有 baseline 可以提案規則修正
Refs: ADR-090 §4.2 E3, MASTER §3.3
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 16:08:34 +08:00 |
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OG T
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ddb902f1ff
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fix(ci+aiops): cd.yaml grep set-e bug + 新增 asset_scanner_job (ADR-090)
CD Pipeline / build-and-deploy (push) Has been cancelled
CI 修復 (b636d3b 第二次 fail 真因):
cd.yaml line 161 ACT_NET=$(docker network ls | grep -E '^GITEA-ACTIONS-...')
act runner 用 'bash -e -o pipefail',grep 無 match 時 exit 1 → 整 step 中斷
(前一次 e7ba8cb fail 是 PG IP 不通,b636d3b 是 grep set-e bug — 兩個不同錯誤)
修復:
ACT_NET=$(... | (grep -E '...' || echo "") | head -1)
把 grep 包在 subshell 並 || echo "" 確保失敗時 ACT_NET 為空字串
新增 asset_scanner_job (ADR-090 § Phase 7 第 1 個 service):
+ apps/api/src/jobs/asset_scanner_job.py (~360 行)
- run_asset_scanner_loop: 每 1h cron,首次延遲 60s
- scan_once: 用 K8sProvider kubectl_get pods --all-namespaces
- UPSERT asset_inventory (asset_key 為 UNIQUE,跨 run 沿用同 asset_id)
- 為每個 active asset 寫 7 維 asset_coverage_snapshot (預設 unknown)
- 寫 automation_operation_log(asset_discovered)
+ main.py lifespan asyncio.create_task() wire
預期解鎖:
- asset_inventory: 從 0 → 數百 (全 namespace pods)
- asset_discovery_run: 每小時 1 筆
- asset_coverage_snapshot: 每筆 asset × 7 dim
- automation_operation_log: 新增 'asset_discovered' op_type
下一階段 (rule_catalog / capacity / compliance scanner) 待 CI 通過後分批提交.
Refs: ADR-090 §4.1, MASTER §3.4 D4, project_blindspot_governance.md
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2026-04-19 14:15:45 +08:00 |
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OG T
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9bfa6fc045
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fix(sweeper): 限制只掃 48h 內 incident，防止歷史舊案洗版 Telegram
CD Pipeline / build-and-deploy (push) Has been cancelled
問題:
首次部署 sweeper 時，找到 117 個無 sweeper_done: 標記的舊 incident
(最舊 2026-04-09，7 天前) → 觸發全部 LLM 分析
舊 incident 資料格式 → OPENCLAW_NEMO timeout → Expert System 降級
confidence=0.2 "降級" → Telegram 連發相同格式告警洗版
修正:
加入 _MAX_INCIDENT_AGE_HOURS=48 過濾
只處理 48h 內的 INVESTIGATING incident
確保 created_at 時區安全（naive → UTC）
2026-04-16 Claude Sonnet 4.6 Asia/Taipei
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-16 01:27:02 +08:00 |
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OG T
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20b3fefca7
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fix(sweeper): 修正 decision key 格式 BUG (decision:INC-* → sweeper_done:INC-*)
CD Pipeline / build-and-deploy (push) Has been cancelled
根本原因:
decision token 實際 key 格式為 decision:DEC-{HEX12}
sweeper 錯誤地查詢 decision:{incident_id} (永遠 = 0)
→ 每 90s 將 186 個 incident 全部列為「未分析」
→ 觸發大量重複 AI 分析請求 (雖 get_or_create_decision 有去重保護)
修正方式:
改用 sweeper_done:{incident_id} 輕量標記 (TTL 1h)
分析完成後才設標記，確保失敗的 incident 下輪仍會重試
get_or_create_decision 內部已有 COMPLETED/READY 去重，雙重保護
2026-04-16 Claude Sonnet 4.6 Asia/Taipei
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-16 01:20:16 +08:00 |
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ce1a4d286e
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feat(sweeper): 新增 Incident Analysis Sweeper — 自動觸發未分析 Incident AI 決策
Gap修復:
Signal Worker 創建 Incident 後，AI 分析只在 GET /api/v1/incidents 被呼叫時觸發
若前端無人訪問，新 Incident 永遠沒有 AI 分析與 Telegram 通知
解法:
新增 src/jobs/incident_analysis_sweeper.py
每 90 秒掃描無 decision token 的 INVESTIGATING incidents
自動背景觸發 get_or_create_decision() — Semaphore(3) 限流，每批最多 5 筆
main.py lifespan 啟動時 asyncio.create_task() 掛載
2026-04-16 Claude Sonnet 4.6 Asia/Taipei
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-16 01:08:30 +08:00 |
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77a92eb469
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feat(P6): 提交 offline_replay_service + model_rollback_service (漏提)
CD Pipeline / build-and-deploy (push) Successful in 14m59s
Phase 6 ADR-087 治理閉環兩個核心服務，
之前建立後沒有 git add，一直是 untracked 狀態。
2026-04-15 Claude Sonnet 4.6 Asia/Taipei
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2026-04-15 22:29:09 +08:00 |
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fb1bbd0e20
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feat(Phase 3): 學習閉環補完 — Root cause 3 + 診斷 feedback + 知識遺忘 + Fine-tune 管線
CD Pipeline / build-and-deploy (push) Has been cancelled
- approval_execution.py: _run_post_execution_verify() 補接 record_verification_result()
Root cause 3 終結：環境驗證結果（success/degraded/failed/timeout）不再孤立
- learning_service.py: 新增 record_verification_result() — 驗證結果 → Redis + Playbook EWMA
- learning_service.py: 新增 record_diagnosis_outcome() — 誤診負向訊號回寫（L3×D4）
- jobs/knowledge_decay_job.py: 新建 30d 知識遺忘 Job（未引用 draft/review → archived）
- services/finetune_exporter.py: 新建每週 JSONL 匯出（EvidenceSnapshot × AgentSession）
- main.py: 掛載 knowledge_decay_loop（24h）+ finetune_export_loop（7d）
- MASTER §8: Phase 3 核心改造項全部落地記錄
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-15 20:57:43 +08:00 |
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OG T
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f045506abd
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fix(flywheel): P2 Approval 逾期不結案 → KM 學習鏈斷鏈修復
CD Pipeline / build-and-deploy (push) Failing after 12m11s
問題根因：
PENDING approval 無人處置超過 48h 後應自動 EXPIRED，
但 get_pending_approvals() 只在用戶開 UI 時觸發，
若無人開 UI → Incident 永遠 PENDING → KM 永遠不寫入
→ Phase 6 SLO human_override_rate 低估，EWMA 缺少負向樣本。
修復：
1. anomaly_counter.py: 新增 "timeout_ignored" disposition 類型，
與 auto_repair / human_approved / manual_resolved 區分
2. incident_service.py: resolve_incident() 新增 resolution_type 參數，
resolution_type="timeout" 時記錄 "timeout_ignored" 而非 "manual_resolved"
3. jobs/approval_timeout_resolver.py (新): 每小時掃描逾期 PENDING approval，
批次標記 EXPIRED，對每筆有 incident_id 的記錄呼叫 resolve_incident("timeout")
4. main.py: startup 掛載 approval_timeout_resolver 排程（interval=3600s）
效果：
- 告警無人處置 48h → Incident 自動結案 → KM 寫入 → EWMA 取得樣本
- disposition="timeout_ignored" 讓 SLO 計算正確區分「AI 建議被忽略」
- 飛輪學習鏈對「無人處置告警」閉環
2026-04-15 ogt + Claude Sonnet 4.6（亞太）
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-15 19:21:21 +08:00 |
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fab65e7d7a
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fix(alerts): PENDING 收斂無 TTL → 老記錄永久封鎖 Telegram 告警
CD Pipeline / build-and-deploy (push) Has started running
根因：find_by_fingerprint 的 PENDING 匹配條件無時間上限，
2026-04-12 建立的 3 筆 PENDING approval records（hit=77/30/17）
持續吃掉所有同指紋告警，造成 2+ 小時 Telegram 靜音。
修正（approval_db.py）：
- PENDING_TTL_HOURS = 24：PENDING 記錄逾 24h 不再收斂新告警
- 原本：OR(status=PENDING, created_at>=30min前)
- 修正：OR(PENDING AND created_at>=24h前, created_at>=30min前)
緊急修復：kubectl exec 直接將 7 筆過期 PENDING 記錄設為 expired，
即時恢復 Telegram 告警流（不等部署）。
Phase 6 AI 自我治理閉環（ADR-087）：
- feat(db): 新增 ai_governance_events 表 + 3 個 index（base.py + models.py）
- feat(svc): ai_slo_calculator.py — 7d 滾動 SLO（success/override/false_neg）
- feat(svc): trust_drift_detector.py — Playbook 信任度極端偏態偵測
- feat(job): kb_rot_cleaner.py — K8s API/Prom metric/老舊 incident_case 腐爛清理
- feat(svc): decision_manager.py — 自我降級守衛（SLO 違反 → 提高門檻/保守模式）
2026-04-15 ogt + Claude Sonnet 4.6（亞太）
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-15 18:56:26 +08:00 |
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db9e304a14
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feat(adr-080): Phase 0 防護欄建立 — AI 自主化飛輪啟動
- docs/superpowers/specs/2026-04-15-MASTER-ai-autonomous-flywheel-v2.md
(1456 行，§0-§8 全填完：42-cell 戰術矩陣、7 Phase 計畫、7 ADR 摘要、
15 KPI、21 Feature Flags、10 風險場景)
- docs/adr/ADR-080-ai-autonomy-flywheel-overview.md
(7 Phase 結構 + 4 北極星 + 7 架構師 Review Gates + Phase 退出條件)
- apps/api/src/core/feature_flags.py
(AIOpsFeatureFlags: P1~P6 總開關全 False + 15 細粒度子開關
is_phase_enabled() / is_sub_flag_enabled() + bool cast 安全)
- apps/api/src/jobs/__init__.py + baseline_snapshot.py
(Phase 0 基線快照 Job：MCP calls / Playbook confidence / general 比例
/ learning loop rate / auto_repair — 寫入 aiops:baseline:latest)
- apps/api/tests/test_feature_flags.py (21 tests — 全綠)
- docs/HARD_RULES.md → v1.9
(新增 Phase 退出條件鐵律：禁止未過 exit conditions 宣告 Phase 完成)
- CLAUDE.md 防失憶閘門 1：強制讀 MASTER §0 Session Resume Protocol
Gate 0 Pass — 21/21 tests green
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-15 12:44:53 +08:00 |
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