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In modern AI systems, understanding what happens inside complex pipelines is critical. This video explains how distributed tracing using Jaeger or Tempo gives complete visibility across your workflow — from Kafka message consumption to orchestration, specialist agents, model inference, and final result publishing. You’ll learn how tracing helps identify bottlenecks, debug failures, and optimize performance in Kubernetes-based AI architectures. Whether you're working with microservices, GPUs, or LLM pipelines, this observability approach ensures reliability and efficiency at scale. This is a must-watch for developers, DevOps engineers, and anyone building production-grade AI systems. #DistributedTracing #Jaeger #GrafanaTempo #Kubernetes #Observability #DevOps #AIInfrastructure #Microservices #OpenTelemetry #CloudComputing #Kafka #SRE #Monitoring #LLM #vLLM