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Quarantine and Manual Review Queue

Holding-and-review process — instantiates Asymmetric Interface Tolerance Calibration

Holds inbound artifacts that are neither cleanly acceptable nor safely rejectable in a queue, so a human resolves the ambiguous middle instead of the parser silently guessing.

Every tolerant interface faces inputs in a grey band — too irregular to accept automatically, not clearly bad enough to reject outright. Quarantine and Manual Review Queue refuses to force those into a binary: instead of accepting them (and quietly widening the protocol) or dropping them (and losing a legitimate message), it holds them where a person decides. Its defining move is to make "I'm not sure" a first-class outcome of the acceptance boundary — a third door between accept and reject — and to convert each held item into a diagnosed, reviewable case rather than a silent guess. The queue is where the interface's uncertainty is parked instead of resolved by accident.

Example

A photo-sharing service runs every upload past an automated safety classifier. Most images clear instantly; a few are unambiguous violations and are refused with a reason. But a band in the middle — a medical photo, satire, a borderline-artistic nude — trips the classifier at medium confidence. Rather than let the parser guess (auto-accept teaches bad actors exactly where the line blurs; auto-reject nukes legitimate posts), those uploads go to a quarantine queue. The image is held, not published; the uploader sees "under review, usually within a day"; a trust-and-safety reviewer sees the item, the classifier's scores, and the specific rule it grazed, then accepts, rejects with a reason, or escalates. The interface stays safe and forgiving because the hard cases are deferred to a human[1] instead of decided by a threshold.

How it works

  • A middle verdict. The acceptance check emits three outcomes, not two: pass, reject, and hold. Only the hold cases enter the queue; it never sees what the interface can already decide.
  • Hold, don't act. A quarantined artifact is neither delivered nor discarded — it is isolated so it can do no harm and lose no information while it waits.
  • Diagnosed on arrival. Each item carries why it landed here (which rule, which score, which ambiguity), so the reviewer starts from a diagnosis rather than a raw blob.
  • Human disposition, fed back. The reviewer's accept/reject decision releases or drops the item and becomes a labeled case the upstream rules can learn from.

Tuning parameters

  • Hold band width — how wide the "send to review" zone is. Wider catches more genuine edge cases but floods reviewers; narrower keeps the queue small but pushes more calls back onto the automated guess.
  • Default on timeout — what happens to an item no one reviews in time: fail-closed (keep holding) protects integrity; fail-open (auto-accept) protects throughput. The choice encodes whether safety or liveness wins under load.
  • Reviewer routing — whether items hit a general queue or are triaged to specialists by risk type; specialization raises decision quality but adds routing latency.
  • Feedback capture — whether dispositions are logged as rule-tuning data or discarded; capturing them lets the automated boundary tighten over time.
  • Requeue vs. escalate — how a reviewer's "still unsure" is handled — bounce to a senior reviewer or hold for policy — so ambiguity has somewhere to go besides a coin-flip.

When it helps, and when it misleads

Its strength is that it absorbs the cases a binary boundary handles worst: it prevents both the silent protocol-widening of auto-accept and the false-negative loss of auto-reject, and it puts human judgment exactly where automated confidence is lowest and the cost of being wrong is highest. It is also the natural home for security holds — anything that might be an attack can wait behind glass rather than be admitted on optimism.

Its failure modes are operational. A queue with no staffing or timeout policy silently becomes a black hole where legitimate items die of neglect, or a pressure valve that gets "cleared" by bulk-accepting under backlog — the classic misuse, where review is nominally required but the queue is drained by rubber-stamp because the SLA is the only thing anyone measures. A quarantine can also lull an interface into leaving its real acceptance rules vague, treating the queue as a substitute for deciding what the contract is. The discipline is to watch the queue's size and age as a signal — a growing quarantine means the acceptance envelope or the upstream producers need fixing, not that the queue needs more seats.

How it implements the components

  • rejection_and_diagnostic_policy — it is the non-binary form of this policy: hold-and-diagnose is the third disposition beside accept and reject, and each held item ships with the reason it was held.
  • integrity_and_security_risk_gate — the queue is where risky or suspicious inputs are isolated pending judgment, so tolerance never means admitting something unsafe on autopilot.

It does not define the accept/reject boundary itself (that's Tolerant Reader / Strict Writer Policy and the Unknown-Field Handling Rule), does not auto-repair the variants it could salvage (Lenient Parser with Canonicalizer), and does not aggregate what it holds into trend telemetry (Malformation Rate Dashboard).

Notes

The queue is a symptom surface, not a cure. A healthy quarantine is small and fast-moving; a chronically large one is the interface telling you its automated boundary is mis-tuned or a specific producer is misbehaving. Treat sustained queue growth as a bug in the acceptance envelope, not as a staffing problem to hire around.

References

[1] Human-in-the-loop — keeping a person's judgment inside an otherwise automated pipeline for the cases where automated confidence is lowest. It is the standard pattern for decisions that are both consequential and genuinely ambiguous, and it is what a quarantine queue operationalizes at an interface boundary. ↩