Industrial environments demand accurate detection of anomalies to maintain product quality and ensure operational safety. Traditional industrial anomaly detection (IAD) methods often lack the flexibility and adaptability needed in dynamic production settings, where new defect types and operational changes continually emerge. Recent advancements in multimodal large language models (MLLMs) have shown promise by combining visual and textual processing capabilities, yet they are often limited by their lack of domain-specific expertise, particularly regarding industry-standard defect tolerances. To overcome limitations, we introduce Echo, a novel multi-expert framework designed to enhance MLLM performance for IAD. Echo integrates four specialized modules: the Reference Extractor retrieves similar normal images to establish contextual baselines; the Knowledge Guide provides critical, industry-specific insights; the Reasoning Expert enables structured, stepwise analysis for complex queries; and the Decision Maker synthesizes information from the preceding modules to deliver precise, context-aware responses. Evaluations on the MMAD benchmark reveal that Echo significantly improves adaptability, precision, and robustness compared to conventional approaches. Our results demonstrate that guided MLLMs, when augmented with expert modules, can effectively bridge the gap between general visual understanding and the specialized requirements of industrial anomaly detection, paving the way for more reliable and interpretable inspection systems.