Two Sigma Architect John Feminella: Five Composable Controls for AI Agent Autonomy Illustrated by Citibank's $900M Error
John Feminella, who works on AI architecture and engineering at Two Sigma, uses the 2020 incident where Citibank accidentally sent about $900 million to Revlon’s lenders instead of an $8 million interest payment to discuss granting AI agents real autonomy without locking everything down. He outlines five composable controls: an agent registry, risk-based review, tool access that follows an agent’s declared purpose, shared firm-wide evals, and classifiers that send humans only the approvals that matter. Views are his own, not Two Sigma’s.
In 2020, Citibank accidentally sent about $900 million to Revlon’s lenders instead of an $8 million interest payment, even though the procedure, the warning pop-up, and three approvers all did what they were supposed to. This famous operational failure highlights the risks inherent in traditional human-in-the-loop processes.
John Feminella, who works on AI architecture and engineering at Two Sigma, uses that story to show how to give AI agents real autonomy without locking everything down. In his recent video episode, he argues that simple restriction is not the solution; instead, sophisticated control layers are required.
Feminella walks through five composable controls designed to address governance gaps in AI agent deployment:
- An agent registry: Clearly identifying which agents exist within the system.
- Risk-based review: Adjusting scrutiny levels based on the potential impact of actions.
- Tool access that follows an agent’s declared purpose: Ensuring agents can only use tools relevant to their stated tasks.
- Shared firm-wide evals: Establishing unified testing standards to validate agent behavior across the organization.
- Classifiers that send humans only the approvals that matter: Filtering routine requests to mitigate "approval fatigue" by escalating only critical decisions.