Distributed Readings

Real-time AI agents with long-lived stateful bidirectional streams cannot be effectively load-balanced using traditional request-response metrics because server capacity is obscured by the nature of continuous conversations.

Developers lack a consistent, standardized way to measure and evaluate the quality of AI agents across both local development and production environments.

Engineers need a systematic way to diagnose performance bottlenecks in TPU-based machine learning workloads to understand whether they are constrained by compute, memory, or network resources.