Chapter 3.1 — What Agentic AI is (and what it is not)¶
🎯 Objective¶
Define Agentic AI rigorously and separate it, unambiguously, from chatbot, copilot and automated workflow. Much of the discussion about "agents" in corporate environments stalls because each participant is using the term for different things.
🧠 Definitions¶
- Agentic AI. An umbrella for systems that receive a goal, use models to decompose or decide, interact with tools and maintain some state to do work with limited autonomy.
- AI Agent. A concrete implementation of Agentic AI with a model, context, tools, memory, policies, runtime, evaluation and observability.
Important: "agent" only makes sense when there is real action, state and intermediate decisions. Without that, there is something simpler.
📊 Comparison¶
| Type | Characteristic | Autonomy | Main risk |
|---|---|---|---|
| Chatbot | Responds in natural language | Low | Incorrect response |
| Copilot | Helps a human with a specific task | Low to medium | Bad suggestion accepted |
| Automated workflow | Runs a deterministic script | Low, deterministic | Incomplete rule |
| Agent | Decides steps, uses tools, maintains state | Medium, controlled | Tool misuse |
| Multi-agent | Several specialized agents | Medium to high | Fragile coordination, cost |
⚠️ Recurring anti-pattern¶
Calling a chatbot an agent is the most common marketing mistake in corporate AI. If the system does not use tools, does not maintain state and does not have its own runtime, it is a chatbot or a copilot, even if some framework calls it an "agent".
The operational question to distinguish them is simple: if I turn off the tools, does the system still do anything useful? If yes, it is probably a chatbot or a copilot. If not, it is an agent.