Managing Complex Failure Analysis Workflows with LLM-based Reasoning and Acting Agents

Failure Analysis (FA) is a highly intricate and knowledge-intensive process. The integration of AI components within the computational infrastructure of FA labs has the potential to automate a variety of tasks, including the detection of non-conformities in images, the retrieval of analogous cases from diverse data sources, and the generation of reports from annotated images. However, as the number of deployed AI models increases, the challenge lies in orchestrating these components into cohesive and efficient workflows that seamlessly integrate with the FA process.This paper investigates the design and implementation of a Large Language Model (LLM)-based Planning Agent (LPA) to assist FA engineers in solving their analysis cases. The LPA integrates LLMs with advanced planning capabilities and external tool utilization, enabling autonomous processing of complex queries, retrieval of relevant data from external systems, and generation of human-readable responses. Evaluation results demonstrate the agent's operational effectiveness and reliability in supporting FA tasks.

@article{dobrovsky2025_2506.15567,
  title={ Managing Complex Failure Analysis Workflows with LLM-based Reasoning and Acting Agents },
  author={ Aline Dobrovsky and Konstantin Schekotihin and Christian Burmer },
  journal={arXiv preprint arXiv:2506.15567},
  year={ 2025 }
}