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This paper proposes value-preserving architectural patterns for agentic AI systems, such as privacy-aware, distributed, and guard-agent architectures, to integrate human-centered values like privacy, fairness, and safety into multi-agent system designs.
This paper proposes directly mapping mature architectural patterns from distributed systems (such as publish-subscribe and message queues) to multi-agent systems to lower the development barrier. It was validated in a course: even students with no distributed systems experience could get started with gRPC and RabbitMQ, achieving an average score above 80%.