Building Explainable, Governable “Agentic AI” Systems That Enterprise Users Can Trust

Why Read This from an HCI Perspective

This article frames agentic AI not as a problem of having a “smarter model,” but as an interaction problem: how people can observe, stop, and approve what the system does. In particular, explainability, audit trails, and human-in-the-loop design are key mechanisms for creating trust and accountability in enterprise UX. For HCI practitioners and researchers, as AI adoption grows, this provides a strong starting point for thinking about what screens, warnings, and approval flows are actually needed.

CIT's Commentary

The core message of this article is that as AI autonomy increases, the interface must become a safety mechanism. As automation gets better, users often want to know not just what is happening, but when they can interrupt and intervene. Without transparent status indicators and clear approval paths, trust collapses quickly. However, in enterprise environments, explainability is not just a matter of being helpful—it is also an operational capability for auditing and for dividing responsibility. Therefore, from a research perspective, we need to evaluate not only the quality of explanations, but also whether explanations actually change intervention behavior. From a product perspective, we must carefully design the trade-off between showing every decision in detail versus revealing only the core risks.

Questions to Consider While Reading

• Q.In high-risk work, how far should minimal explanations go to make users feel they ‘understand’ the AI’s decision?
• Q.What criteria should define the boundary between human-in-the-loop and human-on-the-loop so that real work processes are both safest and most efficient?
• Q.When embedding audit logs and status indicators into UX, what approaches can increase accountability and a sense of control without adding user fatigue?