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This paper proposes an agentic aggregator framework for coordinating electric bus fleet operations, integrating optimization-based scheduling with supervisory AI agents to handle disturbances, tariff adaptation, and value allocation, revealing trade-offs between operational efficiency and profit-oriented pricing.