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Use Case

Compliance-driven review gates for AI workflows

HITL gates aren't a safety net you add when you don't trust the model. In regulated industries, they're a required governance primitive — for financial decisions, PII handling, and content requiring human sign-off. OrchVynt makes them first-class, auditable, and configurable without agent code changes.

How a HITL gate works

When an agent invocation hits a configured trigger condition — confidence below threshold, content flag raised, or explicit rule match — OrchVynt pauses the workflow at the gate. It sends a notification to your configured reviewers and waits for an approval or rejection decision.

On approval, the workflow continues. On rejection, OrchVynt emits a rejection event and returns an error to the caller. If no decision arrives within the configured timeout, the gate auto-resolves via your configured default action (auto-approve or auto-reject).

Every gate event — open, reviewer notified, decision received, timeout triggered — is a structured audit log entry with timestamps and reviewer identity.

hitl.yaml
hitl: gates: - id: financial-decision-review trigger: flags: [financial-action] confidence_below: 0.75 notify: slack_channel: #ai-review-queue webhook: https://hook.company.com/hitl timeout: duration_minutes: 60 on_timeout: auto_reject audit: log: true require_reason: true

When HITL gates belong in your stack

Financial decisions

Any workflow that produces recommendations with financial consequences — trade signals, credit decisions, contract modifications — should route through a human gate before execution.

PII-handling workflows

When agent output contains or acts on personally identifiable information — especially in regulated industries — a HITL gate provides a documented decision point for compliance requirements.

Regulated content review

Healthcare, legal, and financial content requiring professional review before delivery. The gate provides the documented evidence trail that the review happened.

Low-confidence escalation

When model confidence metrics fall below a threshold, automatically route to human review rather than delivering a potentially incorrect result to the end user.

Add review gates to any existing workflow