Interactive Evolutionary Computation (IEC) has long served as a powerful methodology for human-guided design optimization, enabling the iterative refinement of artifacts through user-in-the-loop selection. However, the reliance on human input for fitness evaluation poses significant limitations in scalability, consistency, and exploration depth. In this work, we present a fully autonomous, LLM-driven agentic AI system for evolutionary design—one that eliminates the need for human evaluators by integrating the generative and evaluative capabilities of Large Language Models (LLMs) and Visual Large Language Models (V-LLMs). Our system introduces two novel contributions over traditional IEC approaches: (1) the use of LLMs as intelligent mutation and crossover operators that perform directed transformations of code and prompts, informed by implicit domain expertise; and (2) the use of visual LLMs as automated evaluators and feedback providers, not only to select offspring for breeding but also to generate detailed, expert-level assessments that steer the evolutionary trajectory. This system is instantiated in two domains of high creative complexity: landing-page design and generative visual art. In both domains, LLM-driven transformations generate high-quality, functional, and aesthetically pleasing outputs, while visual LLMs act as domain-aware critics, iteratively refining designs toward optimal forms. Through extensive experiments and qualitative assessments, we demonstrate that our system significantly outperforms traditional IEC and stochastic design pipelines in terms of innovation, convergence speed, and quality of output. This work offers a blueprint for future autonomous design systems, revealing the untapped potential of V-LLMs in evolution-inspired creative processes.