Categorical learners and governance for insurance contracts.

Abstract

Abstract—As large language model (LLM) agents become increasingly modular, the orchestration of their components— such as discrete skills, model providers, and external data servers—presents a complex optimization problem. In this paper, we introduce a category theoretic framework for constructing and optimizing complex agent workflows. We define a strict monoidal category where objects are typed data streams and morphisms represent parameterized agent capabilities. By implementing this framework in Haskell using Generalized Algebraic Data Types (GADTs), we provide compile-time guarantees of well-formedness for agent topologies. Furthermore, we formalize the search for optimal agent configurations as a Bayesian Optimization problem over this categorical space, employing Pareto optimization to balance resource constraints against solution quality.