Google researchers have introduced a groundbreaking multi-agent AI framework designed to solve one of generative video's most persistent challenges: maintaining visual and narrative consistency across long-form content. The system, built on Google's Gemini and Veo models, addresses critical problems that have plagued existing video generation pipelines, including semantic drift (where characters' appearances subtly shift between scenes), cascading errors (where upstream failures corrupt downstream synthesis), and feature drift (where entities gradually change unintentionally across shots). The framework operates as an orchestration layer that treats long-form video generation as a global optimization problem rather than a linear chain of independent processes. Called an "AI video co-director," it employs a hierarchical multi-agent system where an Orchestrator Agent uses a multi-armed bandit algorithm to navigate creative choices across narrative strategy, story structure, and visual tone. Supporting agents handle pre-production storyboarding, keyframe generation, video synthesis, and audio creation, with a multimodal judge evaluating output and feeding quality signals back to refine iterations. Across comprehensive evaluations, Google's framework demonstrated substantial improvements in character persistence and visual continuity, successfully generating minutes-long coherent videos while mitigating the visual drift and error propagation that plague longer narratives. The research, presented at conferences including COLM 2026 and EMNLP 2026, represents a significant advance in making AI-assisted video production more practical. The system's modular design allows compatibility with various foundation models while maintaining safety protections like SynthID watermarking.