For a distributed agentic system across multiple GPU nodes, an event-driven architecture with distributed broker clusters (e.g., Apache Kafka, RabbitMQ cluster) offers the best balance of reliability and performance. The distributed nature of the brokers eliminates single points of failure and bottlenecks, ensuring high availability. This pattern also allows for horizontal scaling to handle high-throughput, low-latency messaging required for performant inter-agent communication in a demanding, multi-node GPU environment. It decouples agents, allowing for asynchronous and resilient communication.

A. Database-based queuing introduces significant latency due to disk I/O and polling, making it unsuitable for high-performance inter-agent communication.

B. Direct TCP connections create a "mesh" that is difficult to manage, scale, and make reliable as the number of agents grows.

D. A centralized message broker is a single point of failure and a performance bottleneck, which undermines the reliability of a distributed system.

1. Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media. Chapter 11, "Stream Processing," details the architecture and benefits of distributed message brokers like Kafka for reliable, scalable, and performant data exchange in distributed systems.

2. NVIDIA Triton Inference Server Documentation. "Architecture and Features". The documentation for multi-node deployments often implies the use of load balancers and message queues (like RabbitMQ or Kafka) to manage and route inference requests between nodes, demonstrating the pattern's applicability in high-performance NVIDIA ecosystems.

3. MIT 6.824: Distributed Systems. Lecture notes on "Fault Tolerance and Replication". The course covers how distributed consensus and replication in systems like distributed message brokers provide higher reliability than centralized approaches.