Compliance Chiefs Face "Examiner-Ready" Test as AI Agents Enter AML Workflows
Financial institutions deploying artificial intelligence agents for anti-money laundering and compliance work are confronting a new challenge: proving to regulators that the technology actually works as advertised.
Peter Piatetsky, cofounder and CEO of Castellum.AI, outlined the regulatory reality facing compliance leaders in a January 21 interview on the Fintech Business Podcast. His firm builds AI agents specifically for AML and know-your-customer workflows, putting him at the intersection of two trends colliding in 2026: the rush to deploy "agentic AI" and regulators' growing scrutiny of how banks actually govern these systems.
The conversation centered on what Piatetsky called the need for "examiner-ready controls"—documentation and governance frameworks that can survive regulatory examination. It's a practical concern that cuts through the hype surrounding AI agents, which have become what Piatetsky and host Jason Mikula described as "one of the hottest topics in fintech, banking and compliance."
For CFOs overseeing compliance budgets, the discussion highlighted a tension that's becoming harder to ignore. Agent-based AI systems promise to automate complex compliance workflows, potentially reducing the armies of analysts currently reviewing transactions and customer files. But deploying them requires building governance structures that regulators haven't fully defined yet.
Piatetsky addressed how compliance leaders should evaluate agent-based solutions, focusing on model governance frameworks adapted for systems that can take actions rather than simply flag risks. Traditional compliance AI tools score transactions or customers; agents are designed to complete entire workflows, from initial alert through investigation to case closure.
The regulatory signals, according to Piatetsky's analysis, point toward a framework where firms need to document not just what their AI agents do, but how they make decisions and what controls prevent them from going off the rails. This matters because AML compliance failures carry both monetary penalties and reputational damage—risks that land squarely on the CFO's desk.
Castellum.AI's approach involves building agents specifically for the compliance use case rather than adapting general-purpose AI tools. The firm's agents handle what Piatetsky described as the full span of AML and KYC workflows, though the interview focused more on governance and evaluation frameworks than specific technical capabilities.
The broader question Piatetsky posed is how agents will reshape compliance programs over the coming years. The technology is advancing faster than regulatory guidance, creating what amounts to a first-mover dilemma: deploy early and potentially face examiner questions about untested controls, or wait for clearer guidance while competitors potentially gain efficiency advantages.
For finance leaders, the calculus involves weighing compliance cost reduction against implementation and governance overhead. Building "examiner-ready" controls for AI agents isn't free—it requires legal review, documentation, testing protocols, and ongoing monitoring that may offset some of the promised efficiency gains, at least initially.
The interview suggests the industry is still in the early stages of figuring out what "good" looks like for AI agent governance in compliance. Piatetsky's emphasis on examiner readiness indicates that the first wave of deployments will likely be conservative, with firms building extensive documentation and control frameworks before scaling these systems broadly.
What's clear is that the conversation has moved beyond whether AI agents will be used in compliance to how they'll be governed when they are. For CFOs, that shift means compliance AI is no longer a future consideration—it's a current budgeting and risk management question that requires answers now.
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