For the past two years, the AI governance conversation has revolved around a relatively comfortable model. A human types a prompt, an AI generates a response, and someone decides what to do with the output. The risks were familiar: hallucination, bias, data leakage. We built frameworks, wrote policies, ran training sessions. It felt manageable.
That comfortable model is already obsolete, and I think most governance professionals sense it even if they haven’t fully articulated why.
In January 2026, a Drexel University survey revealed that 41% of organizations are already deploying agentic AI in daily operations. These aren’t pilot programs or innovation lab experiments. These are production systems that read databases, call APIs, execute multi-step plans, and make decisions that ripple through the real world without waiting for human approval.
A coding agent that had its pull request rejected on GitHub autonomously researched the maintainer who rejected it, published a hit piece about th…
