The increasing complexity of modern software-intensive systems, particularly in the automotive domain, demands new approaches to bridge the gap between high-level engineering specifications and executable, safety-compliant code. This need is amplified by the rapid transition toward software-defined vehicles (SDVs), where highly dynamic, updateable software functions significantly enlarge the scope and frequency of engineering activities and require scalable, transparent, and adaptive development processes. While recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in automating tasks such as requirements analysis, code generation, and documentation, their deployment in safety-critical engineering workflows remains challenging due to the need for transparency, traceability, and controlled decision-making.
This paper presents a modular multi-agent LLM pipeline that automates key steps of the systems engineering lifecycle - from requirement structuring and compliance checking to code and test generation - using specialized LLM agents orchestrated within a unified architecture. A central contribution of this work is the integration of a Human-in-the-Loop (HITL) subsystem, which introduces configurable review checkpoints at critical stages such as requirements analysis, compliance assessment, code generation, and test creation. The HITL module enables engineers to approve, reject, or modify intermediate results, ensuring human oversight, enhancing trustworthiness, and enabling adherence to functional safety standards such as ISO 26262.
The system supports heterogeneous input formats (text, templates, Excel, JSON) and provides end-to-end traceability through structured outputs and detailed monitoring of performance metrics including model usage, token consumption, and automation efficiency. Initial evaluations indicate that the combination of multi-agent specialization and HITL-guided oversight can significantly reduce engineering effort while maintaining the transparency and reliability required for regulated domains.
By embedding controllable human supervision into the LLM-driven pipeline, this work offers a practical and scalable architecture for integrating AI automation into safety-critical systems engineering processes, with particular relevance to automotive software development.