We present a modular, explainable LLM-agent pipeline for decision support that externalizes reasoning into auditable artifacts. The system instantiates three frameworks: Vester's Sensitivity Model (factor set, signed impact matrix, systemic roles, feedback loops); normal-form games (strategies, payoff matrix, equilibria); and sequential games (role-conditioned agents, tree construction, backward induction), with swappable modules at every step. LLM components (default: GPT-5) are paired with deterministic analyzers for equilibria and matrix-based role classification, yielding traceable intermediates rather than opaque outputs. In a real-world logistics case (100 runs), mean factor alignment with a human baseline was 55.5\% over 26 factors and 62.9\% on the transport-core subset; role agreement over matches was 57\%. An LLM judge using an eight-criterion rubric (max 100) scored runs on par with a reconstructed human baseline. Configurable LLM pipelines can thus mimic expert workflows with transparent, inspectable steps.

Game generation and analysis has commonly relied on hand authored rules and heuristics.  This authoring task comes with a high authorial burden, both in the amount of rules and heuristics that need to be authored for decent coverage and in the complexity of authoring these rules.  In this paper I present early work on \textit{Leda} and inductive logic programming system designed to learn these rules, so as to support further generation and analysis.  I present Leda, describe its process, and finally show a sample set of the rules that it learns.