# The 4-layer AI stack

Layer 1: Models (Claude, GPT, Gemini, open-source via API). Layer 2: Context (your data — RAG, vector DB, MCP connections). Layer 3: Orchestration (agents, workflows, automations). Layer 4: Interface (chat, apps, embedded features). Own layers 2–3; rent 1 and 4.

Canonical: https://robauto.ai/learn/ai-architecture/4

_Advanced AI Architecture: Strategy, Stack & Daily Practice — lesson 4 of 20 (DEFINITION)_

Layer 1: Models (Claude, GPT, Gemini, open-source via API). Layer 2: Context (your data — RAG, vector DB, MCP connections). Layer 3: Orchestration (agents, workflows, automations). Layer 4: Interface (chat, apps, embedded features). Own layers 2–3; rent 1 and 4.

Source: [a16z — Emerging LLM App Stack](https://a16z.com/emerging-architectures-for-llm-applications/?utm_source=robauto)

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Machine surfaces: https://robauto.ai/llms.txt · https://robauto.ai/llms-full.txt · https://robauto.ai/.well-known/api-catalog