Agentic Multi-Modal LLMs for Software Comprehension: Structuring Code Summarization with Business Process

Application Understanding task aims to help users comprehend an application’s capabilities by systematically analyzing its artifacts. Ideally, such summaries should align with how the application is used in practice, highlighting essential workflows and functional modules in a structured manner. However, existing automated approaches often fall short of this expectation. Lack of application-specific background and domain knowledge limits the system’s ability to present functionalities meaningfully. To address these challenges, we propose a novel agentic approach leveraging multi-modal LLMs that integrate code analysis, textual artifacts, and domain knowledge to identify key business flow entities—such as programs and tables—within a repository and infer application workflows. This work opens new avenues in LLM-guided software comprehension, bridging the gap between code-centric insights and high-level business process understanding.