AutoIAD: Manager-Driven Multi-Agent Collaboration for Automated Industrial Anomaly Detection
This work addresses the need for reduced manual effort in industrial quality control by providing an automated solution, though it is incremental as it builds on multi-agent and AutoML concepts with domain-specific adaptations.
The paper tackles the problem of automating industrial anomaly detection by introducing AutoIAD, a multi-agent collaboration framework that uses a manager-driven approach to orchestrate specialized agents and a domain knowledge base, resulting in significant outperformance over existing frameworks in task completion rate and AUROC metrics.
Industrial anomaly detection (IAD) is critical for manufacturing quality control, but conventionally requires significant manual effort for various application scenarios. This paper introduces AutoIAD, a multi-agent collaboration framework, specifically designed for end-to-end automated development of industrial visual anomaly detection. AutoIAD leverages a Manager-Driven central agent to orchestrate specialized sub-agents (including Data Preparation, Data Loader, Model Designer, Trainer) and integrates a domain-specific knowledge base, which intelligently handles the entire pipeline using raw industrial image data to develop a trained anomaly detection model. We construct a comprehensive benchmark using MVTec AD datasets to evaluate AutoIAD across various LLM backends. Extensive experiments demonstrate that AutoIAD significantly outperforms existing general-purpose agentic collaboration frameworks and traditional AutoML frameworks in task completion rate and model performance (AUROC), while effectively mitigating issues like hallucination through iterative refinement. Ablation studies further confirm the crucial roles of the Manager central agent and the domain knowledge base module in producing robust and high-quality IAD solutions.