From Unfinished to Done: Bridging the MDE Implementation Gap with Constrained LLMs

Abstract

Model-driven engineering (MDE) excels at ensuring architectural consistency and managing complexity, yet extending generated code skeletons with project-specific business logic often remains a time-consuming manual task. Conversely, large language models (LLMs) offer immense flexibility in coding but may produce vibe-coded results—software that looks correct but fails to adhere to intended architectures and implicit requirements. An open challenge lies in harnessing the adaptability of LLMs to handle implementation details without sacrificing the deterministic correctness of the MDE scaffolding. Previous attempts have largely relied on unconstrained coding assistants or required a human in the loop for interacting with the LLM. To overcome this, we introduce Agentic Gap Filling, a hybrid pipeline where LLM prompts are embedded directly within the model to drive the generation of code skeletons. The generated code contains protected TODO regions, which LLM agents subsequently resolve and a jury of LLM review agents and separate testing agents immediately validate. We integrate this approach into the MontiThings framework, demonstrating a workflow where models define the architecture of a project while artificial intelligence (AI) agents act as developers and reviewers, filling in the logic. This synergy ensures the structural integrity of the generated system while providing the flexibility of LLMs to adapt to various domain requirements. Our evaluation across 32 test configurations demonstrates that this multiagent workflow achieves up to 93.21% functional accuracy after four iterations, while preserving the structural integrity of the generated system.