Towards an Ontology of Normative Role Design for LLM Agent Interactions in Multi-Agent Systems

Abstract

Large Language Models are increasingly deployed as agents in multi-agent systems, where their behavior is shaped largely by the roles encoded in their system prompts. These roles carry normative content, including obligations, permissions, and prohibitions that govern how agents interact in a collective setting. Left unexamined, such design choices can quietly shape how agents behave. Yet no framework exists for identifying normative roles and mapping how they unfold across domains. This thesis develops a conceptual ontology for examining how such roles are designed, what normative content they carry, and what outcomes they produce. A systematic literature review of 724 papers yielded 40 studies, of which 38 were coded as the core of the ontology under a four-layer schema into 93 role conditions, 212 entities, and 516 relational triples. The results reveal partial normative content in most role designs, meaningful differences across simulation domains, and failure modes in nearly half of all role conditions. The thesis contributes (i) a reframing of system prompts as normative documents, (ii) a conceptual ontology of normative role design, and (iii) a reusable coding schema for analysing roles across multi-agent LLM studies. Together, they offer a basis for designing, evaluating, and governing normative role design for LLM agents.