How to Build Agents You Can Trust in Real Life

Why Read This from an HCI Perspective

This article encourages you to see AI not as a mere answer tool, but as an interaction system that plans and acts on its own. For HCI/UX practitioners and researchers, it highlights that the key design question isn’t simply whether the model is smart, but rather when users should intervene, where trust is formed, and how users can recognize failures. In particular, it’s worth reading how permissions, confirmation steps, and transparency create both burdens and benefits in real product experiences.

CIT's Commentary

The core message is that an agent’s interaction structure—not just its raw performance—determines safety. The longer the plan–execute–verify loop is, the more comfortable users may become, but if it’s unclear when they should pay attention, they may miss critical moments. Repeated approval can feel intuitive at first, yet as tasks grow more complex it can lead to frequent warnings that dull users’ attention. That’s why showing the overall plan up front and designing clear intervention paths for users to step in midstream is often more practical. What’s especially interesting is that these principles aren’t only about product design—they also translate into future HCI research challenges, such as measuring how agents communicate uncertainty or how systems correct user trust.

Questions to Consider While Reading

• Q.How can we design and validate the criteria for when an agent should stop and ask the user for confirmation?
• Q.What interface patterns can reduce fatigue from repeated approvals while still preserving users’ sense of control?
• Q.How do ‘show the plan first’ and ‘step-by-step approval’ create different trade-offs across different task types?