DevOps / SRE / Platform · 18.08.2026, 20:25 UTC
Agentic AI has a latency problem that more compute won’t solve
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 18.08.2026 UTC |
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Half of enterprise AI deployments are missing their own latency targets at peak load. This is the headline finding of Akamai’s State of AI Inference 2026 report, which surveyed 200 AI practitioners and found that 82% of organizations say their most critical use cases require end-to-end response times of 500 milliseconds or less. A total of 64% of organizations now require end-to-end response times of less than 250 milliseconds for their most important use cases, yet 50% of deployments are failing to meet these latency demands at peak load.
My colleague Ari Weil, who leads product marketing for our cloud computing business and ran point on that research, summarizes the findings well: “The enterprise AI honeymoon phase is over… they are hitting the latency wall.”
Agentic workflows aren’t a “single round trip”
The latency issue stems from the way agents work. It’s an iterative process, somewhat like a king sending out knights, emissaries, and messengers to conduct the business of the kingdom. There are many comings and goings, not just one person sent on a single round trip.
For instance, when an agent built on a framework like LangChain, CrewAI, or Pydantic AI received a user request, it can fan out into dozens of sequential operations such as a reasoning call, a tool invocation, an API lookup, or a context retrieval. Then an agent may execute another reasoning call to decide what to do with what just came back. Every one of these operations or “hops” that must cross a wide-area network to reach a centralized data center adds latency, and a chain of 50 hops can …