A question for anyone deploying AI agents in production right now 👀 The White House just released a National AI Policy Framework on March 20th. The goal: one federal standard that preempts the growing patchwork of state AI laws. Most of the conversation is focused on what this means for MODELS. Things like Training data, Bias checks, Output filtering. But here's what I think deserves more attention: If this framework becomes legislation.. who in your organization is responsible for what your AI agents DO at runtime? Not what the model says. What the agent actually does. The API calls. The data access. The transactions it executes while it runs. There's a distinction not fully understood by many: Model compliance ≠ Agent compliance Your Model can pass every audit👍 BUT, your Agent can still take actions that violate policy at runtime.. because nobody is governing what the agent does with its tools between the prompt and the response. That middle layer, the one between "what an agent is allowed to be" and "what an agent actually does".. is called RUNTIME GOVERNANCE. It means per action policy enforcement while the agent operates. Not before deployment. Not after something breaks. During execution. And the data backs this up. According to Kiteworks' 2026 research, 63% of organizations can't effectively govern their own AI agents. Gravitee's State of AI Agent Security report found that only 37% to 40% of enterprises have the containment controls needed for agents already running in production. The federal framework just created a clear timeline for when this needs to be solved. The good news?? The organizations building this layer NOW are positioning themselves ahead of enforcement.. not reacting to it. This is an opportunity to lead, not just comply 🔥 What does your runtime governance look like today? 🤖 What would change if you had full visibility into every agent action in 30/60/90 days?