To the point: Agentic AI with its iterative processes and real-time requirements demands infrastructure redesigns that go beyond classical cloud scaling.
The deployment of agentic AI creates new demands on cloud infrastructures: classical cloud models are reaching their limits in performance, consistency and cost efficiency.
AI agents that autonomously plan and execute tasks require continuous communication between models, data sources and external services. This differs fundamentally from static inference workloads, where a request is processed and a result is returned.
The resulting challenges lie in several areas: frequently fluctuating memory utilization through iterative processes leads to inefficient resource utilization and unpredictable costs. Consistency issues arise when agents access external systems multiple times and data can change between queries. Additionally, agent-driven systems typically require latency times in the lower three-digit millisecond range, which standard cloud configurations cannot reliably provide.
For CTOs, this means: cloud infrastructures must be re-evaluated. Approaches such as local model execution, specialized orchestration layers for multi-agent systems, and optimized routing strategies are necessary. Only in this way can the potential efficiency gains from AI agents be realized economically.
Source: www.security-insider.de · Published July 8, 2026
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