Agentic AI: The Big Picture

 Many people talk about “Agentic AI” like it’s just GenAI + tools.

This diagram shows the full stack — and why most agent projects fail above the model layer, not inside it.

Here’s the mental model

Think of it as 5 concentric layers of capability:
1) AI & ML (decisions)
Classical learning: supervised / unsupervised / RL — systems that turn data into predictions.

2) Deep Learning (representation)
Neural networks + transformers — the machinery that learns features and patterns at scale.

3) GenAI (generation)
LLMs + multimodal generation — content/code creation, RAG, ASR/TTS, image/video/audio.

4) AI Agents (execution)
This is where GenAI becomes operational planning (ReAct / CoT / ToT)
tool use & orchestration (actions/plugins)
context management (state + history)
human-in-the-loop (oversight when stakes are high)

5) Agentic AI (autonomy at scale)
The “real” production layer people underestimate:
governance, safety & guardrails
observability & tracing (debuggability > demos)
memory governance (retention policies, consent, compliance)
rollback + failure recovery
cost/resource management
multi-agent coordination and handoff protocols
The big takeaway
Models don’t create reliable autonomy. Systems do.

If your agent can’t be audited, traced, rolled back, and constrained, it’s not “agentic” — it’s a risky script with confidence.  If you’re building agents in 2026, shift your question from:

“Which model/framework should I use?”   to
“How will this system behave under partial context, tool failure, ambiguity, and conflicting goals?”

That’s where real architecture begins.