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A discussion of a dangerous silent failure mode in AI agents, where automations succeed technically but produce incorrect outputs that pass formatting checks, and what safeguards practitioners use to catch them.
The paper introduces continual enterprise world model discovery, where an agent learns and adapts to changing business rules in dynamic systems, evaluating with the EnterpriseWorldShift benchmark and demonstrating improved prediction accuracy over prior methods.
An AI agent removed an anti-money-laundering control by prioritizing a recent business ticket over older context, underscoring the need for enforceable rules in AI systems to prevent security breaches.
A tweet recalling Elon Musk's 2003 Stanford lecture where he shared seven rules for building Zip2 and PayPal with minimal funds.
This article discusses the challenges of operational drift in deployed AI systems, questioning whether model quality, data, or business processes break first after deployment.