Agentic AIGC: A Survey of Visual Generation Agents

Abstract

Visual generation is becoming a general component of AI infrastructure, supporting content creation across design, entertainment, education, science, and industry. Generative and multimodal models have substantially improved single-pass synthesis. At the same time, multi-step visual creation requires spatial precision, identity persistence, temporal coherence, editable structure, factual fidelity, and physical plausibility throughout the creation process. Errors in intermediate artifacts can propagate across subsequent steps, making creation control a central requirement. Model scaling, prompt refinement, additional conditioning, and repeated sampling improve individual outputs; coordinating these capabilities across a task requires runtime evidence to influence subsequent creation decisions. Within AI-generated content (AIGC), we use Agentic AIGC to describe goal-driven content creation organized through a multi-step control loop. This survey focuses on visual generation agents: systems that use runtime evidence from evolving visual artifacts, task environments, and interaction histories to change later creation-relevant action selection or parameterization, including generation, editing, verification, recovery, replanning, and stopping. The defining behavioral property is therefore an observation--action dependency: runtime observations change later action selection. Whether those decisions improve task or constraint outcomes is evaluated separately with matched tools and resource budgets. We organize this emerging field around the control mechanisms and behavioral evidence that connect generative capabilities to autonomous visual creation. Specifically, we formalize this boundary and characterize autonomy through five cumulative levels (L1--L5). A six-component analytical framework (goal understanding, specification, and planning; memory; tool; perception; action; and cross-task self-improvement) structures a cross-domain comparison spanning image, video, 3D/CAD, scientific visualization, structured documents, UI/Web, and related visual-creation domains. Building on this comparison, we analyze training from trajectory supervision and policy initialization through reinforcement learning from multimodal and executable feedback to experience reuse with skill abstraction and transfer. We organize evaluation into four complementary levels: artifact quality, goal and constraint satisfaction, trajectory and decision quality, and system and human-centered evaluation. This synthesis distinguishes behavioral evidence from evidence that feedback improves a creation outcome. Finally, we identify research directions in the scope of visual agency, reliable long-horizon control, cross-task improvement, and evaluation.