fix:批量修复了一些问题

src/llm/agents/orchestrator.py

@@ -97,9 +97,12 @@ class MultiPredictResult:
     reasoning: str | None
     agent_outputs: list[dict]
     agent_weights: dict | None
+    status: str = "success"
     context: str
-    latency_ms: int | None
-    raw: dict | None
+    latency_ms: int | None = None
+    prompt_tokens: int | None = None
+    completion_tokens: int | None = None
+    raw: dict | None = None
 
 
 async def _agent_provider(agent_id: str, *, tier: str) -> LLMProvider:
@@ -158,7 +161,10 @@ async def run_specialists(
     reports: list[AgentReport] = []
     for spec, r in zip(SPECIALIST_SPECS, results):
         if isinstance(r, Exception):
-            logger.warning("agent %s raised: %s", spec.name, r)
+            logger.warning(
+                "专家调用失败 match=%s agent=%s error=%s",
+                header.match_id, spec.name, str(r)[:120],
+            )
             reports.append(AgentReport(agent=spec.name, status="error", analysis=str(r)[:200]))
         else:
             reports.append(r)
@@ -256,8 +262,8 @@ async def predict_match_multi(
     else:
         # 所有专家无数据/均失败:跳过终裁,标记 degraded
         logger.warning(
-            "match %s: 所有 %d 位专家均无有效数据,跳过终裁,标记 degraded",
-            match_id, len(reports),
+            "预测降级 match=%s mode=%s status=degraded experts=%d/%d 均无有效数据",
+            match_id, "multi", ok_reports, len(reports),
         )
         # 无有效专家时不调用 aggregator provider,避免多余开销
         # model 使用 settings 默认值占位(无实际 LLM 调用)
@@ -337,6 +343,13 @@ async def predict_match_multi(
             },
         )
 
+        logger.info(
+            "预测完成 match=%s mode=%s status=%s pred=%s:%s (%s) latency=%sms, experts=%d/%d, prediction_id=%s",
+            match_id, "multi", pred_status,
+            pred.pred_home_goals, pred.pred_away_goals, pred.pred_1x2,
+            latency_ms, ok_reports, len(reports), pred.id,
+        )
+
         return MultiPredictResult(
             prediction_id=pred.id,
             provider=pred.provider,
@@ -350,9 +363,12 @@ async def predict_match_multi(
             pred_1x2=pred.pred_1x2,
             subjective_confidence=pred.subjective_confidence,
             reasoning=pred.reasoning,
+            status=pred_status,
             agent_outputs=pred.agent_outputs,
             agent_weights=agent_weights,
             context=_reports_to_json(reports),
             latency_ms=latency_ms,
+            prompt_tokens=pred.prompt_tokens,
+            completion_tokens=pred.completion_tokens,
             raw=final,
         )