What is an AI Agent?

What you will learn

Understand the key difference between a chatbot and an agent
Know when agents are the right tool and when they are not
Recognise the four parts every agent is built from: model, tools, memory, loop

The simplest definition

An AI agent is a system where a language model (an LLM) runs in a loop — it takes in information, decides what to do next, acts (usually by using a tool), observes the result, and repeats. It keeps going until the task is done. A chatbot answers once; an agent keeps working.

An agent isn't one answer — it's a loop that keeps going until the job is done.

Chatbot vs Agent — the fundamental difference

  CHATBOT
  +------------------------------------------------+
  |  User: "What is the weather?"                  |
  |    |                                           |
  |    v                                           |
  |  LLM: "I don't have real-time weather data."  |
  |  (done -- one round trip)                      |
  +------------------------------------------------+

  AGENT
  +------------------------------------------------+
  |  User: "What is the weather in Amsterdam?"     |
  |    |                                           |
  |    v                                           |
  |  Think: I should call the weather tool         |
  |    |                                           |
  |    v                                           |
  |  Act:   weather_api("Amsterdam")               |
  |    |                                           |
  |    v                                           |
  |  Observe: {"temp": 14, "cond": "cloudy"}      |
  |    |                                           |
  |    v                                           |
  |  Think: I have the data. I can answer now.     |
  |    |                                           |
  |    v                                           |
  |  Answer: "It's 14 deg C and cloudy today."    |
  +------------------------------------------------+

A chatbot does one round trip. An agent loops until the task is complete.

The four components of an agent

Agent = LLM + Tools + Memory + Loop

LLM — the reasoning engine. Decides what to do at each step.

Tools — functions the LLM can request (search, write, query, send). The LLM does not run them — you do. It just asks.

Memory — what the agent knows: the current conversation, past runs, or a knowledge base.

Loop — the cycle of Think → Act → Observe that repeats until the task is done.

Chatbot vs Agent: key differences

	Chatbot	Agent
Input	A message	A goal
Output	A text response	A result (file, action, decision)
Steps	One	Many
Tools	None	Many
Autonomy	You drive	It drives (with guardrails)
Reliability	High, predictable	Needs observability

When to use an agent

Use an agent when:

The task has multiple steps that cannot be hardcoded
The task needs real tools (search, write, query, send)
The task can fail partway and needs to retry or adjust
The steps are not known in advance

Do not use an agent when:

A single LLM call is enough
The steps are fixed — just chain prompts in code instead
Low latency is critical (agents do multiple round trips)
Errors are unrecoverable (agents can make wrong decisions)

Chapter summary

An agent is an LLM + tools + memory + loop
A chatbot does one response; an agent loops until the task is done
The LLM never runs tools directly — it requests them, and your code runs them
Use agents for multi-step, dynamic tasks; use direct LLM calls for simple ones

Check your understanding

What are the four components of an agent?
Name one scenario where you should NOT use an agent.
Who actually runs the tool code — the LLM or your Python code?