Agentic AI means a system that works towards a goal over several steps — it plans, uses tools, looks at what came back, and decides what to do next. A plain chat model answers once and stops.
Intuition
Think about the difference between asking a colleague a question and giving them a task.
Ask a question and you get an answer: "Our refund policy is 30 days." Give them a task — "find out why this customer was charged twice and fix it" — and they do something quite different. They look up the account, read the payment log, notice a duplicate charge, issue a refund, and come back to tell you it is done.
Nothing about the second job needs more intelligence. It needs the ability to take a step, look at the result, and choose the next step. That loop is the whole idea behind an agent.
How it works
The loop
Every agent, no matter how it is built, runs some version of four steps:
flowchart LR
P[Perceive read the goal and what is known] --> R[Reason decide the next step]
R --> A[Act call a tool or API]
A --> O[Observe read what came back]
O --> D{Goal met?}
D -->|No| R
D -->|Yes| F[Final answer]
Step
What happens
Example
Perceive
Take in the goal and current state
"Why was this customer charged twice?"
Reason
Pick the next useful action
"I should look up their recent payments"
Act
Call a tool
get_payments(customer_id)
Observe
Read the result
Two identical charges on 3 March
Loop or stop
Continue, or answer
Issue refund, then report back
The model does not do the acting itself. It requests an action in a structured form, your code runs it, and the result comes back as new text in the conversation. That separation is what makes an agent controllable.
What this chapter covers
What makes something an agent — the perceive, reason, act, observe loop above.
Tool calling and governance — how the model requests actions, and which ones it may take without asking a person.
Single-agent and multi-agent patterns — one worker versus a small team with separate jobs.
Human-in-the-loop and evaluation — where a person must approve, and how you tell whether the agent is actually working.
Where the detailed lessons live
The full agent path — tools, memory, multi-agent orchestration, and the quizzes that go with it — is taught in Module 2.9, Agentic AI and Multi-Agent Orchestration. Study that chapter for depth.
This chapter's job is narrower: it connects those agent ideas to the vision and retrieval work in 4.1 to 4.3, so you can build agents that see as well as read.
What goes wrong
Calling any system with a tool attached an "agent" — without the observe-and-retry loop, it is a single function call with extra steps.
Letting the loop run without a stop condition, so it keeps calling tools until it runs out of budget.
Giving an agent permission to take actions with real consequences before you can see what it did and why.
One-line summary
Agentic AI replaces a single answer with a loop that plans, acts, observes, and repeats until the goal is reached.
Key terms
Agent — A model running in a loop with tools and memory of what it has done.
Tool calling — The model requesting a structured action that your code executes.