Agentic AI has a latency problem that more compute won’t solve
Half of enterprise AI deployments are missing their own latency targets at peak load. This is the headline finding of The post Agentic AI has a latency problem that more compute won’t solve appeared first on The New Stack .
Enterprise AI deployments are struggling to meet their latency targets, even with increased compute power. The Akamai State of AI Inference 2026 report surveyed 200 AI practitioners and found that 50% of deployments fail to meet end-to-end response times of 250 milliseconds for critical use cases, despite 64% of organizations aiming for less than 250 milliseconds.
The issue stems from the iterative nature of agentic workflows, which involve multiple sequential operations that can add significant latency, especially when crossing wide-area networks. Researchers have found that CPU-side processing can account for up to 90.6% of total latency in agentic workloads, and more GPU capacity alone cannot solve this problem.
To address the looming latency wall, new benchmarks are needed that reflect the complexity of agentic workloads. The 500ms latency threshold is not a soft target, as it can determine the success or failure of real-time applications. The solution lies in a tiered architecture, moving agentic execution closer to users and tools, much like Akamai's original solution to the World Wide Wait problem.
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