intermediate~3h

5 Levels of Agentic AI + 4 Layers of Agentic AI

Two complementary mental models for agentic systems — the 5-level maturity progression from basic responder to fully autonomous agent, and the 4-layer architecture stack from LLM foundation to agentic infrastructure.

4 Layers of Agentic AI

Each layer builds on the one below it — LLM basics at the foundation, up through single-agent behavior, multi-agent coordination, to the production infrastructure that wraps it all.

4 Layers of Agentic AI

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builds onbuilds onbuilds onLayer 1: LLMs — tokenization, prompts, APIsLayer 2: AI Agents — tools, ReAct/CoT, planning, memoryLayer 3: Agentic Systems — inter-agent comms, orchestrationLayer 4: Agentic Infra — observability, security, HITL

5 Levels of Agentic AI

Progression of autonomy — how much control shifts from human to LLM at each level.

Who controls the flowWhat the LLM does
Level 1: Basic ResponderHuman guides the entire flowResponds within a fixed, human-defined flow
Level 2: RouterHuman defines the pathsPicks which predefined path to take
Level 3: Tool CallingHuman defines the toolsDecides when and how to use them
Level 4: Multi-AgentHuman defines the hierarchyA manager-agent controls execution flow across sub-agents
Level 5: AutonomousLLM operates independentlyGenerates and executes its own code
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📚 Prerequisites(1)

🎓 Learning objectives

  • Rank agentic systems along the 5-level progression (Basic Responder, Router, Tool Calling, Multi-Agent, Autonomous) by how much control shifts from human to LLM
  • Identify which layer of the 4-layer stack (LLMs, AI Agents, Agentic Systems, Agentic Infrastructure) a given concern belongs to
  • Use both models together to diagnose gaps in an agentic system's design
  • Explain why each outer layer in the 4-layer stack adds reliability, coordination, or governance rather than raw capability

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