Every decade or so, something fundamental shifts in how we think about infrastructure. Not an upgrade, a rethinking.

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Traditional infrastructure automation handles well-understood, repeatable tasks well, but still leaves engineers spending 60-70% of their time on complex, context-dependent problems like incident response, configuration drift, and cross-domain changes. AI reasoning agents offer a qualitatively different capability: instead of executing predefined workflows, they can interpret context, evaluate risk, and choose paths aligned with business intent. The key is agentic orchestration — combining deterministic automation for routine tasks with AI reasoning for complex, open-ended operations — within a governed architecture that builds trust incrementally. Standards like MCP can provide auditable interfaces, and domain-specific knowledge can ground agent reasoning. The argument is that organizations should identify where human judgment is currently required due to variability or complexity, as those are the problems agentic AI is designed to address.

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