refactor(intelligent-eval): reduce nesting complexity in supplement_decision_logs

Extract helper functions _supplement_executing and _supplement_completed
to flatten the nested conditional logic. This improves readability and
makes the code easier to test and maintain.

Addresses code review finding: supplement_decision_logs nested complexity

backend/agenteval/intelligent_eval/decision_logs.py

@@ -130,47 +130,16 @@ def list_decision_logs(eval_id: str, session: Session) -> list[dict]:
     return [_log_to_dict(log) for log in logs]
 
 
-def supplement_decision_logs(session: Session) -> int:
+def _supplement_executing(
-    """Platform audit backfill for decision logs.
+    ev: IntelligentEvalDB,
-
+    sessions: list,
-    方案③的决策日志由 OpenClaw agent 上报（LLM 自主，尽力而为）——异常路径
+    completed: int,
-    （如卡死恢复后重试）agent 可能跳过上报，导致决策过程页面为空。这里按评估
+    estimated: int,
-    状态推导决策并补录：
+    types: set[str],
-
+    session: Session,
-    - EXECUTING：欠账（completed < estimated）补 execute_session，所有会话
+) -> int:
-      完成后补 start_analysis。
+    """Supplement decision logs for EXECUTING evals."""
-    - COMPLETED：历史评估/异常路径可能完全没有决策日志，回填 execute_session
-      （按 plan 时段逐条）+ start_analysis，让旧报告也有决策过程可看。
-
-    只补"该类型缺失"的，不重复；且只记录状态，不改变 agent 的实际执行。
-
-    Returns:
-        补录的决策日志条数。
-    """
-    evals = session.exec(
-        select(IntelligentEvalDB).where(
-            IntelligentEvalDB.status.in_(
-                [
-                    IntelligentEvalStatus.EXECUTING.value,
-                    IntelligentEvalStatus.COMPLETED.value,
-                ]
-            )
-        )
-    ).all()
-
     added = 0
-    for ev in evals:
-        plan = ev.get_plan() if ev.plan else {}
-        estimated = plan.get("estimated_sessions", 0)
-        sessions = session.exec(select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)).all()
-        completed = sum(1 for s in sessions if s.status == "completed")
-        types = {
-            x.decision_type
-            for x in session.exec(
-                select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
-            ).all()
-        }
-        if ev.status == IntelligentEvalStatus.EXECUTING.value:
     if "execute_session" not in types and completed < estimated:
         _append_row(
             ev.id,
@@ -191,8 +160,20 @@ def supplement_decision_logs(session: Session) -> int:
             session,
         )
         added += 1
-        elif ev.status == IntelligentEvalStatus.COMPLETED.value:
+    return added
-            # 历史回填：completed 评估决策日志全缺失时，按时段补 execute_session
+
+
+def _supplement_completed(
+    ev: IntelligentEvalDB,
+    sessions: list,
+    completed: int,
+    estimated: int,
+    plan: dict,
+    types: set[str],
+    session: Session,
+) -> int:
+    """Supplement decision logs for COMPLETED evals (historical backfill)."""
+    added = 0
     if "execute_session" not in types:
         slots = plan.get("time_distribution") or []
         if slots:
@@ -233,3 +214,54 @@ def supplement_decision_logs(session: Session) -> int:
         )
         added += 1
     return added
+
+
+def supplement_decision_logs(session: Session) -> int:
+    """Platform audit backfill for decision logs.
+
+    方案③的决策日志由 OpenClaw agent 上报（LLM 自主，尽力而为）——异常路径
+    （如卡死恢复后重试）agent 可能跳过上报，导致决策过程页面为空。这里按评估
+    状态推导决策并补录：
+
+    - EXECUTING：欠账（completed < estimated）补 execute_session，所有会话
+      完成后补 start_analysis。
+    - COMPLETED：历史评估/异常路径可能完全没有决策日志，回填 execute_session
+      （按 plan 时段逐条）+ start_analysis，让旧报告也有决策过程可看。
+
+    只补"该类型缺失"的，不重复；且只记录状态，不改变 agent 的实际执行。
+
+    Returns:
+        补录的决策日志条数。
+    """
+    evals = session.exec(
+        select(IntelligentEvalDB).where(
+            IntelligentEvalDB.status.in_(
+                [
+                    IntelligentEvalStatus.EXECUTING.value,
+                    IntelligentEvalStatus.COMPLETED.value,
+                ]
+            )
+        )
+    ).all()
+
+    added = 0
+    for ev in evals:
+        plan = ev.get_plan() if ev.plan else {}
+        estimated = plan.get("estimated_sessions", 0)
+        sessions = session.exec(
+            select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)
+        ).all()
+        completed = sum(1 for s in sessions if s.status == "completed")
+        types = {
+            x.decision_type
+            for x in session.exec(
+                select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
+            ).all()
+        }
+        
+        if ev.status == IntelligentEvalStatus.EXECUTING.value:
+            added += _supplement_executing(ev, sessions, completed, estimated, types, session)
+        elif ev.status == IntelligentEvalStatus.COMPLETED.value:
+            added += _supplement_completed(ev, sessions, completed, estimated, plan, types, session)
+    
+    return added