GOAM: Game-Oriented Agentic Modeling for Turn-Based Game AI

We present GOAM (Game-Oriented Agentic Modeling), an architecture for turn-based game AI that separates strategic orchestration from tactical execution under an explicit certainty-based routing scheme. A macro process maintains longer-horizon objectives and game context; a micro process handles immediate action execution, cached patterns, and reactive play. Routing between these layers is governed by a certainty score that limits expensive language-model deliberation to genuinely ambiguous positions, reducing cost while preserving strategic coherence across a full game trajectory. We formalize the GOAM architecture; its macro/micro decomposition, certainty model, decision routing, and feedback structure, and examine a derivative implementation deployed as an LLM bridge for turn-based strategy. Supporting supplementary materials report isolated micro-agent results via NOESIS-based experiments in chess and tic-tac-toe. Together, these contributions position GOAM as a principled agentic architecture for turn-based game AI and motivate a broader empirical validation program.