John Wilganowski
1c1d2ff21b
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.
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
"""ReviewGate: build the human-review queue from prioritized findings."""
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from typing import Dict, List, Optional
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from backend.review.policy import build_audit_sample, requires_review
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def _finding_item(issue: Dict, blocking: bool, reasons: List[str], kind: str) -> Dict:
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issue_id = issue.get("issue_id") or "unknown"
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return {
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"review_item_id": f"finding:{issue_id}",
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"kind": kind,
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"blocking": blocking,
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"reasons": reasons,
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"payload": issue,
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}
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def build_review_queue(memory_snapshot: Dict, prioritized: List[Dict], decisions: List[Dict],
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limit: Optional[int] = None) -> List[Dict]:
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queue: List[Dict] = []
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for issue in prioritized:
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reasons = requires_review(issue)
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queue.append(_finding_item(issue, bool(reasons), reasons, "finding" if reasons else "audit_finding"))
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if limit is None:
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for item in build_audit_sample(memory_snapshot, prioritized):
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queue.append(item)
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else:
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for item in build_audit_sample(memory_snapshot, prioritized, limit=limit):
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queue.append(item)
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return queue
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