feat: add traceable scoped AIOps diagnosis

mvp/demo/README.md

@@ -78,6 +78,43 @@ Expected result:
 - `success` is `true`.
 - A later trace query shows `data.session.feedback` as `useful`.
 
+## 4. Run AIOps Alert Diagnosis
+
+```powershell
+$aiopsSessionId = "mvp-demo-aiops-payment-cpu-001"
+$aiopsBody = @{
+  sessionId = $aiopsSessionId
+  alertName = "HighCPUUsage"
+  service = "payment-service"
+  severity = "P1"
+  description = "服务 payment-service 的 CPU 使用率持续超过 80%，当前值为 92%。实例: pod-payment-service-7d8f9c6b5-x2k4m。"
+  timeRange = "last_15m"
+  userRequest = "请结合 Prometheus 活动告警、system-metrics 日志和知识库生成告警分析报告。"
+} | ConvertTo-Json
+
+Invoke-WebRequest `
+  -Method Post `
+  -Uri "http://localhost:9900/api/ai_ops" `
+  -ContentType "application/json" `
+  -Body $aiopsBody
+```
+
+Expected result:
+
+- The SSE stream starts with a `session` message containing `mvp-demo-aiops-payment-cpu-001`.
+- The stream later contains an AIOps alert analysis report focused on the supplied `HighCPUUsage/payment-service` payload.
+- A trace query for the same session id returns `data.session.agentFlow` as `AI_OPS`.
+- `data.session.answer` contains the final alert analysis report when a report is generated.
+- `data.toolInvocations` contains evidence tools such as `lookup_knowledge`, `query_logs`, or `query_metrics` when the runtime uses them.
+
+Query the AIOps trace:
+
+```powershell
+Invoke-RestMethod `
+  -Method Get `
+  -Uri "http://localhost:9900/api/diagnosis/$aiopsSessionId/trace"
+```
+
 ## Demo Story
 
 The important interview story is:
@@ -92,3 +129,14 @@ one session id
 -> feedback
 -> trace API for replay and audit
 ```
+
+The AIOps story uses the same audit spine:
+
+```text
+one session id
+-> alert payload
+-> AIOps planner/executor execution
+-> evidence tools
+-> alert analysis report
+-> trace API for replay and audit
+```

mvp/demo/aiops-alert-acceptance.md

@@ -0,0 +1,38 @@
+# AIOps Alert Acceptance Case
+
+## Goal
+
+Validate that the legacy AIOps endpoint can act as a traceable alert-triggered diagnosis entry.
+
+## Input
+
+- Session id: `mvp-demo-aiops-payment-cpu-001`
+- Endpoint: `POST /api/ai_ops`
+- Profile: `mvp-demo`
+- Alert:
+
+```json
+{
+  "sessionId": "mvp-demo-aiops-payment-cpu-001",
+  "alertName": "HighCPUUsage",
+  "service": "payment-service",
+  "severity": "P1",
+  "description": "服务 payment-service 的 CPU 使用率持续超过 80%，当前值为 92%。实例: pod-payment-service-7d8f9c6b5-x2k4m。",
+  "timeRange": "last_15m",
+  "userRequest": "请结合 Prometheus 活动告警、system-metrics 日志和知识库生成告警分析报告。"
+}
+```
+
+## Acceptance Criteria
+
+1. The SSE stream emits a `session` message containing the requested session id.
+2. The AIOps run creates or updates `diagnosis_session` with `agent_flow = AI_OPS`.
+3. The persisted session query contains the alert name, service, severity, time range, and description.
+4. If a final report is generated, `diagnosis_session.answer` contains that report.
+5. `GET /api/diagnosis/{sessionId}/trace` returns the AIOps session, ordered agent steps, and ordered tool invocations.
+6. In payload mode, the report focuses on `HighCPUUsage/payment-service`; unrelated active alerts may appear only as related risk or context, not as separate full root-cause sections.
+
+## Known Limits
+
+- This slice does not add a Verifier Agent to AIOps.
+- Full runtime verification still depends on valid DB, Redis, Milvus/Zilliz, model, and embedding configuration.