When ChatGPT appeared in November 2022, most conversations about AI were about chatbots: systems you converse with, that answer questions, that generate text. The model receives a message and produces a response. That is all.

AI agents are something fundamentally different. They do not just respond: they make decisions, execute actions in external systems and observe the results of those actions to decide the next step. The difference is not one of degree: it is one of nature.

The fundamental difference

A chatbot is reactive and passive: it waits for an input, produces an output, finishes. The human does everything else: interprets the response, decides what to do with it, executes the necessary actions.

An agent is active and autonomous (to a degree): it receives an objective, breaks down the problem, decides what actions to take, executes them using available tools, observes the results, adjusts the plan and continues until the objective is completed or the agent determines it cannot proceed without human help.

CHATBOT:
  User → Message → Model → Response → User
  (the user does everything else)

AGENT:
  User → Objective → Agent → Plans → Acts
                           ↑            |
                           └── Observes ←┘
  (the agent executes multiple steps autonomously)

The practical difference: if you ask a chatbot “research the three best cloud provider options for our company,” it will give you general information about cloud providers. If you ask the same of a well-configured agent, it can search the web, compare current prices, review recent reviews, consider your specific requirements and produce an analysis adapted to your situation — all autonomously.

The perceive–decide–act cycle

AI agents operate on a continuous cycle known as the reasoning-action loop (ReAct, from Reasoning and Acting):

1. Perceive. The agent receives the objective and the context of the environment: what tools it has available, what has happened so far, what the current state of the task is.

2. Reason. The agent analyses the situation and decides what to do next. This step can include breaking the objective into sub-objectives, identifying what information is missing, selecting which tool to use.

3. Act. The agent executes the chosen action: calls an API, searches the web, writes a file, executes code, sends an email.

4. Observe. The agent receives the result of the action and incorporates it into its context. The result may confirm it is on the right track, indicate that the plan needs adjusting, or reveal new information.

5. Repeat. The cycle continues until the objective is completed or the agent determines it cannot proceed without human help.

This cycle can execute dozens or hundreds of times to complete a complex task. At each iteration, the language model does the reasoning part; the tools do the acting-in-the-world part.

Agent architecture

An AI agent has three main components:

The language model (the “brain”). The LLM is what reasons, plans and decides. The models that work best as an agent brain must be good at following complex instructions, reasoning in multiple steps and using tools correctly.

The tools (the “arms”). These are the agent’s action capabilities: what it can do in the world. Typical tools include:

  • Web search
  • Code execution
  • Database access
  • Sending emails or messages
  • Reading and writing files
  • Calls to external APIs (calendars, CRM, third-party services)

Memory. Agents need to remember what they have done and learned during the execution of a task. There are several types:

  • Working memory: the active context of the current task (within the LLM’s context window)
  • Short-term memory: results of previous actions within the session
  • Long-term memory: information that persists across sessions (implemented with external databases)

What an agent can do

The most mature agent use cases in 2025–2026:

Automated research. The agent searches multiple sources, synthesises information, identifies contradictions and produces a report. Tasks that would take a human hours are completed in minutes.

Workflow automation. Processing invoices, updating CRMs, generating periodic reports, responding to certain types of emails following predefined criteria.

Development assistance. Agents that can read the entire codebase, identify bugs, write fixes, run tests and verify that changes do not break anything.

Data analysis. The agent receives an analysis objective, decides which queries to run on the database, executes them, interprets the results and produces conclusions.

Planning and coordination. Multi-agent systems where several agents with different roles collaborate: one researches, another writes, another verifies the information, an orchestrator agent coordinates the process.

The risks autonomy introduces

The autonomy of agents introduces risks that simple chatbots do not have.

Error amplification. In a chatbot, an error is an incorrect response the human can discard. In an agent, a reasoning error can translate into real actions with consequences: an email sent, a file modified, a transaction executed.

Lack of transparency. The more steps the agent executes autonomously, the harder it is to understand why it made certain decisions. The “black box” becomes more opaque.

Scope creep. An agent trying to complete an ambiguous objective may take actions the user did not anticipate. “Clean my inbox” could be interpreted in very different ways.

Tool dependency. If a tool fails or returns incorrect results, the agent may build on that incorrect information in later steps, compounding the error.

The most important design principle for agents: human oversight at the highest-impact points. Agents are most useful when they do the heavy lifting autonomously but ask for confirmation before irreversible or high-risk actions. In the next chapter we will look at how this works in practice.