Tag: agent-ux

Action receipts make agentic work inspectable by recording what changed, where, when, with which tool, and how to verify or reverse the result without exposing hidden reasoning.

Function calling becomes a user-facing affordance when AI can trigger real actions, so schemas, previews, labels, and action states must make consequences understandable.

Capability discovery should teach users what an agent can and cannot do through progressive, contextual demonstrations rather than vague claims of intelligence or giant tool catalogs.

Confirmation design for AI agents should reserve human attention for consequential or ambiguous actions, using risk, reversibility, uncertainty, and scope to decide when to ask or act.