AI Agents
If you have used ChatGPT or Claude, you know what a chatbot is : you ask a question, it answers, and then it waits for you. An AI agent goes one step further : you give it a goal, and it works towards that goal by itself, taking actions, observing the results, and correcting course until the job is done.
A useful analogy : a chatbot is like a very knowledgeable consultant on the phone. They can tell you how to fix your invoice process, but you still have to do everything yourself. An agent is like an employee at a desk in your office : they can open the files, use the tools, do the work, and come back to you when it is finished : or when they need a decision from you.
The three building blocks : perceive, reason, act
Cloud providers converge on the same definition. AWS describes the traditional agent architecture as a loop of three modules : perceive, reason, act. Google Cloud defines an agent as an application that achieves a goal by combining a model ( the reasoning engine ), a set of tools ( to interact with the outside world ) and a goal it pursues with limited supervision.
- Perceive : the agent takes in information : your instructions, the content of files, the result of a search, an error message.
- Reason : the language model decides what to do next : which tool to use, in what order, and whether the goal is reached.
- Act : the agent executes : it writes a file, sends a query, runs a program, fills in a form.
The crucial part is that these three steps form a loop. After acting, the agent perceives the result of its own action and reasons again. If something failed, it sees the failure and tries to correct it. That closed loop : act → observe → verify → correct : is what separates an agent from a chatbot that simply produces text and stops.
Agentic AI vs generative AI
IBM draws a helpful line between the two terms. Generative AI produces content : text, images, code : in response to a prompt. Agentic AI uses that same generative capability inside a system that makes decisions and takes actions autonomously to pursue a goal. Generative AI is the engine ; agentic AI is the car.
| Generative AI ( chatbot ) | Agentic AI ( agent ) | |
|---|---|---|
| You provide | A question or prompt | A goal |
| It returns | An answer | A result ( work actually done ) |
| Tools | None : it only writes text | Files, web, programs, databases |
| Errors | You spot them and re-ask | It observes and corrects itself |
| Supervision | Every step | At the level you decide |
What an agent needs to work well
An agent is only as useful as what surrounds it. Four ingredients matter in practice :
- Tools : the agent must be able to do things : read and write files, browse the web, call business systems. Without tools, it is just a chatbot again.
- Memory : instructions and context that persist : so the agent remembers your rules, your conventions and its own past mistakes from one session to the next.
- Guardrails : explicit permissions deciding what the agent may do alone, what requires your approval, and what is forbidden. Humans set the rules ; the agent executes within them.
- Verification : a good agent proves its work before declaring it done : it runs the test, checks the output, reads the result.
These four ingredients are exactly what tools like Claude Code industrialize : our Claude Code guide walks through each of them in plain language.
Where agents already work today
Concrete, current examples : an agent that reads incoming support emails, looks up the customer's history and drafts a contextual reply for a human to approve ; an agent that monitors a data pipeline every morning, investigates failures and documents what it fixed ; an agent that audits a website page by page and publishes the corrections. These are not prototypes : they are systems we build and operate at eaQbe for our own operations and for clients.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot answers questions ; an agent pursues goals. The agent has tools ( files, web, programs ), takes actions with them, observes the results and corrects itself : a chatbot only produces text and waits for your next message.
Are AI agents autonomous?
Within the limits you set, yes. A well-designed agent works under explicit permissions : some actions it may take alone, others require your approval, others are forbidden. Autonomy is a dial you control, not an on/off switch.
What is an example of an AI agent in business?
A customer-support agent that reads each incoming ticket, retrieves the relevant account data, drafts an answer and routes the edge cases to a human. It perceives ( the ticket ), reasons ( what does this customer need? ) and acts ( drafts, retrieves, routes ) : the full loop.
Going further
- The next page explains how single agents combine into agentic workflows.
- To see agents in action on your own projects, our hands-on Claude Code & Agentic AI training builds one with you in a day.