We present Pixie, a context-aware, multi-agent large language model (LLM) architecture designed to enhance digital marketing operations across real-time and offline activities. Pixie uses a primary orchestration agent alongside task-specific and reconciliation/reviewer agents to support a broad range of marketing tasks. These agents combine zero-shot and few-shot prompting with marketing data, proprietary expert strategy guidelines, and documentation for proprietary statistical models. We experiment with agentic workflows ranging from single-agent to multi-agent systems. We evaluate them using assessments by trained marketing experts and measurements of LLM output variance, focusing on accuracy, consistency, and domain-specific problem-solving. Results show that Pixie's multi-agent framework improves decision quality and operational efficiency in digital marketing workflows. We assess Pixie on common digital marketing processes, including root-cause analysis and reporting, campaign research, and planning. The framework is designed to support additional specialized agents for other marketing tasks and adapt to evolving digital marketing needs.

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