The increasing complexity of digital commerce presents challenges in pricing, customer engagement, and revenue optimization. Existing solutions such as dynamic pricing, automated negotiation, customer intelligence, and revenue management systems often operate independently, leading to fragmented decision-making and limited coordination. Many approaches also lack governance, explainability, and collaborative intelligence required for enterprise-scale autonomous decision-making. This paper proposes BazaarAI, a Governance-Aware MultiAgent Revenue Decision Operating System that unifies customer intelligence, pricing optimization, revenue management, opportunity detection, governance enforcement, explainable AI, and continuous learning within a single enterprise framework. The system transforms customer and operational signals into governed revenue actions through collaboration among specialized agents responsible for customer, pricing, revenue, opportunity, governance, and learning objectives. The proposed architecture integrates signal intelligence, customer intelligence, pricing intelligence, revenue intelligence, opportunity intelligence, agent collaboration, action optimization, and continuous learning. A governanceaware decision engine ensures compliance with organizational policies, profitability goals, pricing constraints, and risk requirements while maintaining transparency through explainable decision traces. The primary contribution of this research is a unified enterprise framework that combines collaborative agent intelligence, governance-aware decision-making, explainability, and adaptive learning within a Revenue Decision Operating System. The framework provides a foundation for future enterprise AI systems capable of autonomous and accountable revenue optimization in dynamic business environments.

Keywords—Pricing Intelligence, Negotiation Intelligence, Multi-Agent Systems, Revenue Optimization, GovernanceAware AI, Explainable AI, Decision Support Systems