The Power of Agentic AI to “Restore” What We Can’t Replace

Why Read This from an HCI Perspective

This article frames AI not as a ‘smart automation tool,’ but as a new UX layer that connects between long-standing, complex interfaces. That’s especially important for HCI practitioners and researchers. Even if performance is high, if a system’s state is misrepresented, users end up with greater distrust and more mistakes. It’s a great case for thinking through when an agent should intervene, when it should hand off to a human, and how failures should be explained.

CIT's Commentary

The core of this piece isn’t what the model knows, but how safely people can operate a system through that model. The idea of an agent slipping into legacy systems as an ‘invisible middle layer’ is particularly interesting: convenience increases, but state transparency can decrease—making it easier for users to get lost. So the value of an agent isn’t just task delegation; it depends on how clearly it reveals failure modes and retry paths. In this context, more than the model’s raw accuracy, designing where users can intervene and how recovery flows can be made possible is the more important research challenge. In Korea as well, in environments with complex procedures—such as Naver, Kakao, and financial or administrative services—‘AI that can be checked and rolled back midstream’ may be a more realistic solution than ‘AI that finishes in one go.’

Questions to Consider While Reading

• Q.What interface cues would be most effective for helping users quickly understand the current state when an agent wraps a legacy system?
• Q.How can we split responsibilities safely between actions the agent automatically corrects and the points where a human must intervene?
• Q.What HCI evaluation methods would be needed to measure the trustworthiness and recoverability of such agents in real services?