If I used an AI system to recruit for my company, I would probably hire a man named Jared who played lacrosse in high school. That's according to research referenced by Jessica Grose of The New York Times, in a video worth watching if you haven't seen it.
When an algorithm audited an automated resume-screening tool, it didn't find that leadership, problem-solving, or domain expertise were the top predictors of success. It found two variables: being named Jared and having played high school lacrosse.
Why? Because without proper guardrails, AI models do what they do best: identify statistical patterns in historical data, even when those patterns are completely arbitrary or reflect legacy socio-economic biases.
This is why enterprise AI adoption cannot happen without robust AI governance:
Proxies hide in plain sight. Even if you strip protected attributes like gender or race, an unmonitored model will latch onto proxy variables — names, zip codes, extracurriculars — to recreate the same bias.
Policies on paper don't stop runtime errors. A compliance document won't catch a biased correlation during live inference. Governance must be active and built directly into every AI workflow. It needs active verification.
Guardrails protect trust. AI adoption stalls when stakeholders and regulators lose confidence in system outputs. Guardrails, explainability, and human oversight are what make deployment safe at scale.
If you are deploying AI across your organization, whether in hiring, customer operations, or risk assessment, how are you auditing what your models are actually optimizing for?
This is the gap between a policy and an enforced control. Cortega's argument check runs on every tool call, inspecting the actual request, not just whether the caller was authorized to make it, and fails closed on ambiguity rather than assuming good intent. Our agent governance page covers how that two-layer check works in practice.