How to effectively use AI agents for regulatory documentation?

The article explores the challenge of information retrieval in large legal document sets using AI agents. The author proposes using hierarchical indices based on the structure of the laws themselves (chapters, articles, clauses), which significantly reduces the volume of data processed by the model. Instead of scanning the entire corpus, the agent navigates the table of contents 'tree,' selecting the necessary branches. This approach, comparable to B-trees in databases, allows for the efficient use of even small language models (1.5–3 billion parameters). The author argues that navigating structured data turns the task from complex reasoning into simpler classification, improving answer accuracy and reducing computational costs. The method relies on pre-indexing via regular expressions, making the system fast and scalable for handling legally significant documentation.