Enterprise Agentic AI Demands Hybrid Coordination Layer Beyond Public Cloud
sanjaykalra · x · 2026-08-31
Jitendra Golani argues that enterprise AI cannot thrive on public cloud scale alone; it requires a robust coordination layer across hybrid infrastructure, especially when moving from predictive models to production-grade Agentic AI.
Key Points:
- Sub-Millisecond Execution: Real-time AI agents require ultra-low-latency loops. Routing sensitive operational data through public cloud endpoints introduces performance jitter that undermines autonomous workflows.
- Dynamic Model Orchestration: Enterprise agents must chain models across AWS, Azure, GCP, neoclouds, and on-prem systems. A coordination-first architecture allows teams to route and swap models based on cost and performance without re-engineering integrations.
- Predictable Governance & Economics: Egress fees and fragmented compliance models destroy ROI. Decoupling orchestration from cloud hosting provides strict runtime controls and predictable unit economics.
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