Agentic Video Generation

Agentic video generation studies how AI agents can turn goals, scripts, or prompts into videos by planning scenes, choosing conditions for video models, and checking the results. Common approaches use language and vision models to coordinate specialized agents, maintain story or scene memories, and refine clips with visual critiques, simulations, or reward-based learning. These methods target long-form storytelling, physical actions, advertising, and interactive video, and are evaluated with benchmarks for visual quality, narrative coherence, consistency, and task success, alongside human judgments. A central challenge is making feedback trustworthy: generated videos can drift across shots, and automated judges may be misled by an agent’s execution traces rather than the visual evidence.