Enterprise AI agents are stalling — not because of model performance, but because of permissioning. Every agentic workflow eventually hits the same wall: what is this agent allowed to touch, on whose behalf, and how does the system know?Workday’s answer is to make its existing system of record the governance layer for agents. Gerrit Kazmaier, the company’s president for product and technology, told VentureBeat in an interview that customers often struggle when they cobble together solutions for their agents. “Sana makes sure the integrity of the approvals and security model is always adhered to,” Kazmaier said. “Frankly, that’s where we see customers struggling when they try to build do-it–yourself AI by just accessing raw data, so the richness of the security model gets lost, and the results become overly broad.”Workday, which launched Sana in March, expanded its partnership with Google to bring its Sana agent system of record to the Gemini Enterprise — so agents built on Sana are also discoverable there.Architecting accuracyKazmaier said the biggest hurdle they faced was ensuring agent accuracy, especially for HR and finance users. “Almost right is not acceptable,” Kazmaier said. “Think about paying people correctly, closing the books or managing work schedules reliably.” Accuracy is harder to evaluate here than in most AI contexts. Policy configurations, role-based security, and organizational hierarchies are deeply interrelated — a small error compounds. And unlike most generative AI outputs, HR and finance queries often lack a correction loop. By the time a paycheck processes incorrectly or an interview is scheduled wrong, the damage is done.Workday addressed this by building Gemini in as its base reasoning layer, then adding its context engine and business process logic on top. Workday also added verification and classification models that “interrogate” outputs before execution. Accuracy and identity, it turns out, are the same question: does the sy …
