Interlocal agreements are one of the primary instruments through which local governments formalize collaboration for public service delivery, yet the institutional and financial content encoded in these contracts has remained inaccessible to systematic analysis at scale. This paper introduces an end-to-end computational pipeline for classifying intergovernmental agreements by institutional form and extracting financial relationships between principals and agents in service contracts. Applied to Iowa's 28E archive (N = 21,629), the largest dataset of interlocal agreements in the United States, the pipeline combines LLM-based summarization and classification across LLaMA 3.1, GPT 5.2 Pro, and Gemini 3 Pro on a four-class classification task that distinguishes agreements as either service contracts, resource sharing agreements, joint operations agreements, or new joint entity agreements. We also identify the financial principal and agent in these agreements and contracts, as well as the resulting dollar amounts and represent them on a directed network. The resulting financial network is organized around a small number of dominant service providers, with counties serving as the most structurally versatile actors, and cities as predominantly principals. By rendering the content of Iowa interlocal agreements analyzable at scale for the first time, this pipeline establishes a reusable methodology that researchers and state agencies can apply to track how public dollars move across local governments and to identify entities that depend heavily on a small number of providers.

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