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Many optimization patterns from the mainframe era are being revived in agentic AI systems. When a core resource is powerful but expensive and slow, we wrap it in caching, abstraction layers, routing, and edge computing. Today, that expensive core is LLM inference.
The familiar four moves are already visible:
Every AI agent is built from 12 distinct layers. Most tools look impressive in demos but fail in real work because one or more layers are missing or weak.
The framework breaks down into four parts:
