An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software

As Large Language Models (LLMs) continue to advance in their ability to process both natural and programming languages, they show promise for translation tasks in domains with strict compliance requirements. Yet ensuring consistency in legally critical settings remains challenging due to inherent limitations such as natural language ambiguity and the tendency to hallucinate. This paper explores an agentic approach that leverages LLMs for the development of legal-critical software. We use U.S. federal tax software as a representative case study, where natural language tax code must be translated precisely into executable logic.