Traditional AI benchmarks measure model performance in isolation but fail to capture whether users actually trust and can work effectively with AI agents. Drawing on UX research experience at Microsoft and Cisco, the author argues that interaction-layer evaluation is the missing piece for agentic AI success. Three key dimensions are identified: intent alignment (does the agent understand what users actually want?), confidence calibration (does the agent signal uncertainty appropriately?), and correction patterns (what do user edits reveal about agent failures?). UX research methods like think-aloud protocols, correction taxonomies, diary studies, and contextual inquiry are proposed to complement automated metrics. With Gartner predicting 40% of agentic AI projects will be canceled by 2027, the author contends that trust — not model capability — is the real bottleneck.
Sort: