Add required human review gate to the Agent pipeline.

Agent web jobs now stop after Brain consolidation and enter needs_review
with a persisted review queue (blocking: high-severity, low-confidence,
sensitive-category findings; audit sample of clean clusters). Humans
decide confirm/reject/unsure/needs_clarification via new review API and
frontend queue; a finalizer applies decisions (rejections suppressed with
reason codes), performs bounded targeted reruns for clarifications,
drafts RFIs only for kept issues, and only then marks the job done and
sends the final email. Two-phase email (review-required, then final
report), per-decision feedback labels with redacted aggregate metrics,
restart recovery from job artifacts, and CLI --no-review bypass.
Classic pipeline unchanged. 65 non-LLM tests.

backend/review/gate.py

@@ -0,0 +1,31 @@
+"""ReviewGate: build the human-review queue from prioritized findings."""
+
+from typing import Dict, List, Optional
+
+from backend.review.policy import build_audit_sample, requires_review
+
+
+def _finding_item(issue: Dict, blocking: bool, reasons: List[str], kind: str) -> Dict:
+    issue_id = issue.get("issue_id") or "unknown"
+    return {
+        "review_item_id": f"finding:{issue_id}",
+        "kind": kind,
+        "blocking": blocking,
+        "reasons": reasons,
+        "payload": issue,
+    }
+
+
+def build_review_queue(memory_snapshot: Dict, prioritized: List[Dict], decisions: List[Dict],
+                       limit: Optional[int] = None) -> List[Dict]:
+    queue: List[Dict] = []
+    for issue in prioritized:
+        reasons = requires_review(issue)
+        queue.append(_finding_item(issue, bool(reasons), reasons, "finding" if reasons else "audit_finding"))
+    if limit is None:
+        for item in build_audit_sample(memory_snapshot, prioritized):
+            queue.append(item)
+    else:
+        for item in build_audit_sample(memory_snapshot, prioritized, limit=limit):
+            queue.append(item)
+    return queue