[2603.28731] SAGAI-MID: A Generative AI-Driven Middleware for Dynamic Runtime Interoperability

Computer Science > Software Engineering arXiv:2603.28731 (cs) [Submitted on 30 Mar 2026] Title:SAGAI-MID: A Generative AI-Driven Middleware for Dynamic Runtime Interoperability Authors:Oliver Aleksander Larsen, Mahyar T. Moghaddam View a PDF of the paper titled SAGAI-MID: A Generative AI-Driven Middleware for Dynamic Runtime Interoperability, by Oliver Aleksander Larsen and 1 other authors View PDF HTML (experimental) Abstract:Modern distributed systems integrate heterogeneous services, REST APIs with different schema versions, GraphQL endpoints, and IoT devices with proprietary payloads that suffer from persistent schema mismatches. Traditional static adapters require manual coding for every schema pair and cannot handle novel combinations at runtime. We present SAGAI-MID, a FastAPI-based middleware that uses large language models (LLMs) to dynamically detect and resolve schema mismatches at runtime. The system employs a five-layer pipeline: hybrid detection (structural diff plus LLM semantic analysis), dual resolution strategies (per-request LLM transformation and LLM-generated reusable adapter code), and a three-tier safeguard stack (validation, ensemble voting, rule-based fallback). We frame the architecture through Bass et al.'s interoperability tactics, transforming them from design-time artifacts into runtime capabilities. We evaluate SAGAI-MID on 10 interoperability scenarios spanning REST version migration, IoT-to-analytics bridging, and GraphQL protocol conversio...