A software requirements analysis document comprises various functional and non-functional requirements of stakeholders and is often expressed in structured natural language. If these requirements are ambiguous or deviate from the end goals, it leads to significant delays and inefficiencies during the software development process. Traditionally, control flow graphs (CFGs) are used to represent the logical flow and complexity of a software system. However, they are generated from the source code and require human effort and experience. In this paper, we propose a novel multi-LLM fusion agentic framework for automated CFG generation directly from natural language software requirements. This serves as a supporting tool, enabling early identification of ambiguities, inconsistencies, and structural issues before implementation. The framework leverages multiple large language models (ChatGPT, LLaMA 3, and Gemini) to extract and interpret control logic. It employs an additional ChatGPT agent to fuse outputs along with contextual information from a vector database via a retrieved augmented generation (RAG) mechanism. To ensure semantic validation, we proposed evaluation metrics: (i) function accuracy, (ii) event accuracy, and (iii) event consistency. Cyclomatic complexity is also used to assess a program’s structural soundness. Experimental results demonstrate that cfgLLM generated valid CFGs, offering assistance for early-stage software design and requirement validation.