This paper presents the theoretical foundation and conceptual architecture for the application of Multi-Agent Systems (MAS) in automated generation of Scalable Vector Graphics (SVG) from natural language text descriptions. The paper is conceived as the initial phase of a broader research program to be developed within a doctoral dissertation, establishing theoretical foundations, identifying research gaps in the literature, and proposing a system architecture whose implementation and formal evaluation will be the subject of future scientific work. The proposed multi-agent architecture consists of five specialized agents collaborating on the decomposition of the complex creative task of SVG generation: Natural Language Understanding Agent (NLU Agent), Composition Planning Agent (Layout Agent), SVG Primitive Generation Agent (SVG Generator Agent), Validation and Optimization Agent (Validator Agent), and Coordination Agent (Orchestrator). The paper includes a preliminary benchmarking experiment that empirically documents the limitations of single-agent models and motivates the multiagent approach.