SAGAI-MID: A Generative AI-Driven Middleware for Dynamic Runtime Interoperability
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 t...
| Authors | Oliver Aleksander Larsen, Mahyar T. Moghaddam |
|---|---|
| Primary category | cs.SE |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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