Everyone is excited about how capable AI agents are becoming. But the real challenge ahead isn't intelligence, it is underlying data, and more importantly, the governance around AI Agents. As AI moves from answering questions to taking actions, companies run into a new set of questions fast: can we trust an autonomous decision, can we explain why it happened, where does a human need to be in the loop, and how do we enforce boundaries once agents are operating at scale, not just in a pilot. These are important question for one AI Agent. meshIQ industry analysts estimate average number of AI agents to be in thousands for a mid-size corporation by the end of 2027. This matters now because models themselves are becoming commoditized. As LLMs get faster, cheaper, and easier to swap, competitive advantage stops coming from the model and starts coming from what that model is safely allowed to do once it's connected to real enterprise systems. An agent is only as useful as the corporate context it can reach, and enterprise data is fragmented across CRMs, ERPs, tickets, logs, and legacy systems, middleware among them. In that world, trusted context becomes the moat, and governance is what makes that context safe to hand an agent in the first place.