From Artifacts to Actors: A Design Theory for Modeling Algorithmic Actors in Agentic Service Systems

Agentic AI is reconfiguring service systems by introducing computational entities that interpret situations, select among alternatives, and shape service trajectories with varying degrees of decision authority. Established service-design representations lack a category for such entities, treating them either as passive artifacts or as human actors. Following design science research, this paper develops a design theory for modeling algorithmic actors in agentic service systems. It comprises a four-level framework—service-system embedding, actor differentiation, decision authority, and responsible AI governance—with operational classification rules, five design principles for materiality, and testable propositions. The theory is instantiated as an extension of service blueprinting and formatively evaluated in three real-world configurations whose primary algorithmic actors hold assistive, delegated, and autonomous authority with different forms of embodiment. Interviews with six informants, documents, and observations provide preliminary evidence that the extended blueprint can represent algorithmic actors, authority allocations, authority transitions, and governance mechanisms at the process-step level. The cases illustrate that the same algorithmic actor can hold different authority levels across process steps and suggest two propositions for future testing: that risk types vary with embodiment and that escalation architectures become more elaborate as authority increases.