AI Systems Pyramid

Nate four-layer hierarchy for matching problems to the right level of AI sophistication. Bottom: custom GPTs and Claude Projects (reactive, you stay in the loop). Next: pure automations with zero AI (deterministic, rule-based, maintenance-free). Next: AI workflows (fixed path with one or more intelligent nodes for classification, writing, or decision-making). Top: autonomous AI agents (goal plus tools plus decision-making, only justified when path is unpredictable). Decision tree: do I need to be in the loop? Is every step pure logic? Is the order of operations fixed? The pyramid exists to counter the beginner tendency to jump straight to agents when simpler layers would deliver the same outcome with lower cost and better reliability.

Source references

  • [src-008] Nate Herk cluster — Nate Herk — AI consulting and business cluster (11 videos)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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