---
title: Agentic AI in compliance: Earning the right to remove the human
source_page: https://www.sardine.ai/whitepapers/agentic-ai-in-compliance
canonical: https://www.sardine.ai/whitepapers/agentic-ai-in-compliance
format: text/markdown
date: 2026-10-06T16:30:00.000Z
description: Sardine's AI agent now decisions 100% of a U.S. bank's sanctions and PEP alerts with zero missed hits. See how the bank cut human review from 100% to 10%.
---

**Quick links:** [Human page](https://www.sardine.ai/whitepapers/agentic-ai-in-compliance) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/blog) · [Demo](https://www.sardine.ai/demo)

---

# Agentic AI in compliance: Earning the right to remove the human

**Published:** 2026-10-06T16:30:00.000Z · **Categories:** Agentic AI, Compliance, AML, Community Banks

Sardine's AI agent now decisions 100% of a U.S. bank's sanctions and PEP alerts with zero missed hits. See how the bank cut human review from 100% to 10%.

Most "AI for compliance" pitches stall the moment a human still has to review every decision. If that's still true, the most expensive part of the process never actually changes.
Working with a regulated U.S. bank, Sardine put an AI agent on live sanctions and PEP screening and reduced human oversight one evidence-based step at a time. To our knowledge, this is one of the first U.S. institutions to fully automate sanctions and PEP decisioning, an area regulators watch closely.
A few of our insights:
Live since April 2026, the agent now decisions 100% of sanctions and PEP alerts in real time, auto-resolving the roughly 89% that are clear false positives and escalating the rest.
Manual reviews are down about 95% for sanctions alerts and about 90% for PEP alerts.
Human oversight tapered from 100% of the agent's decisions at launch to 10% within four months, and only after each stage cleared its own success criteria.
Zero missed hits. No confirmed sanctions or PEP match has ever been auto-cleared, across more than 6,000 live decisions.
On a blind backtest of 459 previously resolved alerts, the agent's recommendation matched the human decision 95.8% of the time. On the live decisions it commits to, it agrees with reviewers 99.9% of the time.
The staging method itself: write the plan down, backtest true-positive detection, validate false positives on real alerts, go live under full review, then taper oversight by measured agreement, framed the whole way in the Model Risk Management language examiners already use.
The full report covers the four-stage governance framework, the confusion matrix behind the zero-false-negative claim, how the bank made its case to two different types of examiners, and the open questions we haven't fully settled yet, including how far a taper like this can go.
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