In 2016, I explored how engineers might inspect the behavior of a service robot powered by a network of specialized agents, services, and physical skills.

The system could listen to a person, interpret their intent, generate a response, and coordinate speech, gestures, and movement—but much of that activity remained invisible. I designed “Train of Thoughts,” an exploratory diagnostic experience that traced an interaction from human input through classification, planning, dialogue, and execution.

Through system mapping, visual experimentation, interaction design, and prototyping, I translated logs and machine activity into a navigable timeline of decisions, service calls, results, and failures. Research with IBM engineers ultimately revealed limited demand for the proposed experience, reinforcing a principle that continues to shape my work today: AI interfaces must be grounded in real user needs, not only in what technology can visualize.