This paper presents a novel multi-agent artificial intelligence framework designed for real-time fashion market intelligence through automated sentiment analysis and competitive benchmarking. The proposed system employs three specialized autonomous agentsData Collector, Analytics Engine, and Strategic Recommendation Agentoperating in a coordinated architecture to provide continuous market monitoring and actionable business insights. The framework demonstrates significant improvements in processing efficiency (85% accuracy in sentiment classification), response time (30-second cache refresh cycles), and strategic decision-making capability. Experimental results show the system successfully processes multi-platform social media data, competitor pricing intelligence, and brand perception metrics in real-time, achieving 78.4% balanced accuracy in sentiment classification and 40% improvement in adaptive decision-making compared to traditional approaches. The system architecture incorporates advanced natural language processing, hierarchical reinforcement learning, and dynamic visualization components within a Streamlit-based dashboard. Performance evaluation demonstrates robust scalability, autonomous operation, and practical applicability for fashion retail market intelligence.