Agentic Influence Operations: A Multi-Dimensional Governance Framework for Generative AI-Enabled Information Disorder in Multimedia Ecosystems

Abstract

Generative artificial intelligence (GenAI), synthetic media, social bots, and agentic AI are turning information disorder from a content-verification problem into a governance problem for multimedia information ecosystems. Research on fake news, deepfakes, GenAI misuse, and cyber-enabled influence has developed in largely separate streams, and none of these streams offers an integrated governance model for influence activity coordinated through AI-supported workflows. This study develops such a model through a structured evidence synthesis of 35 peer-reviewed sources published between 2019 and 2026, identified through four structured discovery queries and citation chaining and appraised with evidence-type-specific criteria. The synthesis identifies five mechanisms through which AI reshapes information disorder: influence orchestration, ambiguity production, selective persuasion, hybrid human–machine dissemination, and a trust paradox in countermeasures. On this basis, the study defines agentic influence operations as workflow-level risks and organizes them in a twelve-dimension framework that links AI capabilities, content modality, platform infrastructure, data access, evidentiary trust, organizational harm, and governance control points. It also sets out construct boundary conditions, five propositions with explicit evidence status, a managerial control set, and a phased implementation roadmap. Only the proposition on synthetic media and uncertainty about authentic evidence has direct empirical support; the others are offered for testing through simulation, experimental, design-science, and expert-validation studies.