Compliance Chiefs Navigate Regulatory Fog as AI Agents Enter AML Workflows

Financial institutions deploying artificial intelligence agents for anti-money laundering and compliance work face a fundamental challenge: regulators are signaling openness to the technology while offering little concrete guidance on how to document and govern it.

That tension emerged in a recent conversation between Fintech Business Weekly's Jason Mikula and Peter Piatetsky, cofounder and CEO of Castellum.AI, a firm building AI agents for financial crime compliance. The discussion, published January 21st, centered on what Piatetsky called the "examiner-ready controls" problem—how compliance leaders translate vague regulatory signals into audit-ready governance frameworks.

The stakes are significant for CFOs and compliance chiefs. AI agents—systems that can autonomously execute multi-step workflows rather than simply flagging items for human review—promise to reshape how banks and fintechs handle Know Your Customer checks and transaction monitoring. But the technology arrives without a regulatory playbook, leaving financial institutions to reverse-engineer acceptable practices from examiner comments and enforcement actions.

Piatetsky outlined how Castellum.AI's agents currently power AML and KYC workflows, though the interview transcript provided limited technical specifics. The broader challenge he described will sound familiar to any finance leader who lived through the first wave of machine learning adoption: regulators want innovation, but they also want explainability, audit trails, and someone to blame when things go wrong.

The model governance question looms particularly large. Traditional compliance models—say, a transaction monitoring system that scores wire transfers—operate within established regulatory frameworks. Agentic systems that make sequential decisions across multiple data sources don't fit neatly into those boxes. How do you document a model that adjusts its own workflow based on what it finds? What does "model validation" mean when the agent's decision tree evolves?

Piatetsky's advice to compliance leaders evaluating agent-based solutions focused on distinguishing genuine capabilities from vendor promises—a familiar refrain for CFOs who've sat through too many AI demos where the technology works perfectly until you ask it to handle your actual data. The interview suggested firms should pressure vendors on specifics: What exactly can the agent do autonomously? Where does it hand off to humans? What does the audit trail look like when an examiner asks why a particular transaction was cleared?

The conversation also touched on how agents might reshape compliance programs over the coming years, though again with limited concrete predictions. The safe bet: more automation of routine investigative work, freeing human analysts for complex cases. The harder question: whether regulators will accept that shift, or whether they'll insist on human review at every decision point, effectively neutering the technology's value.

For CFOs, the practical takeaway is less about Castellum.AI specifically and more about the broader pattern. AI agents are arriving in compliance departments whether finance leadership is ready or not. The firms that figure out governance frameworks first—how to document agent decisions, how to validate their accuracy, how to explain them to examiners—will have a meaningful advantage. The firms that deploy agents without that groundwork will likely provide the cautionary tales everyone else learns from.

The regulatory signals Piatetsky referenced remain deliberately ambiguous, which is either prudent caution or regulatory paralysis depending on your perspective. Either way, compliance chiefs can't wait for clarity that may never arrive. They're building the governance frameworks in real time, one examiner conversation at a time.