Structured input tasks, such as form filling and data entry, are common across domains but can be time-consuming and cognitively demanding, especially when users must convert free-form descriptions into rigid formats. This paper explores using Large Language Model (LLM) agents to support transforming unstructured natural language and document-based inputs into structured outputs aligned with predefined schemas. We describe a modular pipeline that takes as input a natural language description, optional supporting documents (e.g., PDFs, spreadsheets), and a form schema. The LLM processes these inputs to generate structured key-value pairs with confidence scores, which can be used to populate forms automatically while allowing users to review and adjust the output. The system follows a mixed-initiative approach, emphasizing human oversight and editable results. Principles from human-centered AI and adaptive interface engineering guide our design. Rather than presenting a full empirical evaluation, this work contributes a modular architecture and design perspective on embedding LLM agents into structured input workflows, highlighting integration challenges and early feasibility observations in real-world contexts such as the MICS project. The approach is domain-agnostic and compatible with existing infrastructures through the Model-Context Protocol (MCP).