Async Inference: Why the Future of AI Agents Runs on Queues, Not Real-Time

The article exposes a fundamental tension in current AI inference: nearly all infrastructure is optimized for real-time, synchronous chat, where every millisecond of latency is penalized.

But as agents evolve from chat assistants into background workers—scanning codebases overnight, enriching CRM rows, processing documents—the vast majority of tokens will flow through a queue, not a synchronous request-response loop.

The existing serving stack optimizes for cold-start and low latency, not throughput, making it structurally expensive for the batch workloads that agents increasingly demand.