Agentic Visual Generation: From Generative Models to Agentic Control
Visual generation is evolving from generative models used through a single invocation into agentic control processes that can plan, select tools, inspect intermediate synthesized outputs, revise failures, and reuse prior experience. In most existing systems, the controller is an LLM or VLM, while visual generation models serve as tools or executors. However, existing work lacks
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Visual generation is evolving from generative models used through a single invocation into agentic control processes that can plan, select tools, inspect intermediate synthesized outputs, revise failures, and reuse prior experience. In most existing systems, the controller is an LLM or VLM, while visual generation models serve as tools or executors. However, existing work lacks
This research highlights the shift from static generative models to dynamic, agentic systems capable of planning and self-correction, which is crucial for developing more autonomous and robust AI applications. Understanding this paradigm shift is key for ML engineers to design and implement next-generation visual generation systems.