A Modular Multi-Agent LLM Architecture for Text-to-Diagram Generation and User-Guided Refinement

Context: Recent advances in LLM-based diagram generation increasingly rely on coordinated agent systems rather than single-model prompts. Objective: This work highlights how modular multi-agent architectures improve reliability, semantic grounding, and iterative refinement in text-to-diagram workflows. Method: We analyze a pipeline composed of specialized agents for interpretation, synthesis, validation, and correction, each contributing a bounded and inspectable transformation. Results: The agent system provides deterministic validation, structured reasoning, and controlled refinement loops that outperform monolithic LLM generation. Conclusions: Multi-agent LLM pipelines represent a robust foundation for precise, verifiable diagram generation and serve as a reproducible alternative to single-pass text-to-diagram models.