Partnering with Edra: Context for Agents at Scale

Why Read This from an HCI Perspective

This article is especially meaningful for HCI/UX practitioners and researchers because it frames AI agents not as ‘smart models,’ but as ‘work interfaces that can be used immediately in the field.’ In real organizations, procedures differ, exceptions must be handled, and tacit knowledge matters—so performance alone isn’t enough. What becomes crucial is how users can see the system’s state, make edits, and intervene. It’s a useful case study for uncovering friction points and design challenges during real productization.

CIT's Commentary

What’s particularly interesting is that the core value of agents isn’t simple automation, but how they make ‘organizational memory’ visible. The approach of building a living knowledge base from support tickets, emails, logs, and chat records highlights a key point: rather than focusing solely on making the model bigger, it’s more important to determine what users can trust and what they can correct. In safety-critical work, a transparent and editable structure is a major advantage—yet it also introduces the risk that incorrectly learned rules could propagate throughout the entire workflow. That’s why such systems shouldn’t be designed only around accuracy metrics; they also need clear intervention paths, traceable change history, and a way to roll back when things fail.

Questions to Consider While Reading

• Q.How can we prevent incorrect rules or exceptions from becoming ‘locked in’ to a knowledge base during the process of automatically collecting an organization’s tacit knowledge?
• Q.What kind of interface would be appropriate to help users trust an agent’s judgments while still allowing them to easily intervene and correct when needed?
• Q.In fast-changing work environments like Korea’s Naver, Kakao, or domestic startups, in what ways would a ‘living knowledge base’ approach be more advantageous—or more difficult?