Scaling real-time AI agents with session-aware load balancing

Building real-time AI agents introduces unique challenges compared to traditional web APIs, as it involves managing continuous, bidirectional streams of data rather than isolated requests. This shift requires rethinking infrastructure and load balancing approaches because the conventional metrics like QPS (queries per second) and CPU utilization do not adequately capture the complexities of stateful AI streams, which involve ongoing interactions such as audio chunks, transcripts, and model outputs. In real-time AI systems, load balancing must consider both the current resource usage and the future workload commitments, leading to a hybrid model that combines active session counts with traditional utilization metrics. This approach helps manage the load more effectively by distributing active sessions evenly across backends, preventing overloads and ensuring that each backend can handle its committed tasks. As AI agents become more prevalent, infrastructure must evolve to support these continuous, live conversations, requiring load balancers to synthesize various signals to make informed decisions and maintain system efficiency.

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