With the growing popularity of game services that require constant content updates to retain players, automating the generation of adaptive playable characters has become an urgent task. This article examines existing approaches to character generation, including evolutionary algorithms, and in-session adaptation systems. Current solutions are limited by their inability to provide sufficient long-term adaptation to individual player styles and their reliance on manual design. To address these limitations, we propose a three-component system that integrates: player action modeling based on gameplay replays using reinforcement learning (RL) agents, character generation through combinatorial mechanics and parameter balancing, automatic validation via simulations to assess balance and alignment with a player’s individual style. This work synthesizes contemporary research, highlighting the potential of generative methods to reduce development costs for game services. The results could accelerate prototyping and enhance the long-term viability of live-service projects.