Article: Stateful Continuation for AI Agents: Why Transport Layers Now Matter

Agent workflows fundamentally change how we think about platform infrastructure. I've watched teams deploy LLM applications assuming single-request patterns apply to multi-turn agent loops, only to face cascading latency and cost issues at scale. The insight here is brutally simple: when agents iterate dozens of times per task, transport overhead compounds viciously. Stateful continuation and context caching aren't optimization niceties anymore—they're architectural requirements. This reshapes data platform decisions. You can't ignore message queue selection, connection pooling strategies, or whether your observability layer tracks agent state across turns. For teams building on managed LLM platforms, this means evaluating whether streaming, batching, or session-pinning capabilities exist natively. For those managing custom inference layers, transport becomes a first-class citizen alongside model serving. My recommendation: audit your current agent deployments for hidden transport costs, then baseline latency per agent turn. You'll likely discover 40-60% of execution time is infrastructure friction, not inference.