Avoiding God Agents: An Empirical Solution to Architecting Standalone Multi-Agent Systems with Multi-Aspect Agents

God agents, i.e., agents that accumulate excessive responsibilities, have become an increasingly common problem in modern multi-agent systems and agent frameworks. As business logic grows, interaction scenarios become more complex, and agents are expected to be increasingly self-contained, their implementations tend to evolve into large orchestration hubs that are difficult to understand, maintain, and extend. Existing platforms such as AutoGen attempt to alleviate this problem by decomposing a god agent into multiple collaborating agents, whereas many other MAS frameworks pay little attention to this issue during architectural design. To address the god-agent problem, this paper proposes a fine-grained modular architectural pattern for implementing agents and MASs that systematically separates functional concerns into independently managed aspect entities while preserving the logical identity of agents within a standalone environment. The architecture consists of a rootstock and multiple scions. Each scion manages a specific category of agent functionality by maintaining the corresponding aspect entities, while the rootstock enables collaborative execution by supporting interactions among all aspect entities. The proposed architecture is implemented and evaluated through GUSU, a real-time strategy game engine comprising three scions responsible for collision detection and path planning, game logic, and rendering, respectively. A quantitative software metrics study comparing GUSU with 17 representative agent frameworks, including JADE and several recent LLM-based multi-agent frameworks, shows that GUSU exhibits favorable structural characteristics commonly associated with maintainability and modularity while preserving the semantic integrity of individual agents, rather than relying on task decomposition into multiple agents or multi-agent orchestration. At the agent-class level, GUSU demonstrates fine-grained implementation characteristics, while at the system level, it shows a relatively balanced trade-off among coupling, cohesion, complexity, inheritance, and implementation size, ranking second among the evaluated MASs according to the adopted multivariate analysis.