AuditLoop
Work-boundary evidence for AI-operated workflows
AuditLoop turns AI reliability work into concrete operating records: boundaries, failure notes, escalation rules, and launch evidence.
Definition
What AuditLoop is
A lightweight workflow for deciding what an AI system may do, when it must stop, and what evidence reviewers need.
What it measures
- Task boundaries, refusal behavior, confidence, and ambiguous cases.
- Failure modes, severity, recurrence, and operational impact.
- Human escalation points and evidence needed for review.
What it produces
- Evaluation criteria and test cases.
- Failure-mode report with unresolved risks.
- Escalation map and implementation priorities.
Research link
Where the arXiv paper fits
The paper supplies background on overconfidence and calibration. AuditLoop turns that concern into review questions teams can operate.
Delivery
What an engagement can look like
Work-boundary sprint
Five business days for one AI-operated workflow, ending in allowed actions, stop rules, test cases, failure modes, and unresolved risks.
Boundary review
Two-week review for teams preparing a launch, vendor decision, governance discussion, or internal automation rollout.