What is an AI agent and how does it differ from a chatbot?

Published on 2026-10-20 by frfrand

Agentes de inteligencia artificial explicados: modelos, herramientas, permisos, planificación, ejemplos y límites frente a un chatbot tradicional.

An artificial intelligence agent is a system that uses a model to interpret goals, make decisions about steps and, when it has authorized tools, perform actions on other systems. It is not simply a chat box with long answers.

The practical difference appears when you move from asking "how do I do this?" to asking "review this and execute the necessary operations". But to act for real, the system needs tools and permissions.

A real-life example

Imagine you manage an online store and ask a chatbot "how can I view pending orders?". The chatbot can explain where to click and which filters to use.

An agent connected to the right APIs could query pending orders and prepare a summary. If it also has authorized access to make changes, it could tag orders, generate reports or notify the team.

Artificial intelligence does not receive magical access to your accounts. Each operation depends on available tools and specific authorizations.

What pieces does an agent consist of?

In a typical architecture there is a model, instructions, tools, context and an execution mechanism that coordinates the steps.

The model helps interpret the task. Tools can query databases, call APIs, execute code, or interact with applications. The execution mechanism decides when to finish, how to handle errors, and whether it should request an approval.

Implementations vary. Some systems follow fixed sequences; others allow the model to select actions dynamically. There is no single technical definition that covers everything the market calls an agent.

What is the difference with traditional automation?

A classic automation works well when the steps are clearly defined. For example: "when an order arrives, send a notification."

An agent can be useful when you need to interpret unstructured information or choose between different tools. For example: "review today's incidents and classify those that require attention."

This does not mean that AI should control absolutely everything. Many sensitive actions —charging, deleting data, granting permissions— should retain deterministic rules and, when appropriate, human approval.

Why does it need tools?

A language model produces text and can select actions when interfaces are offered to it. It does not automatically know if your server is turned on nor can it restart it by responding with a command.

Tools expose concrete operations. A tool could list applications, query a record, or request a deployment through an authorized service.

MCP is one of the protocols that allow presenting tools and context to AI systems. But MCP is not the complete agent: it is a way to connect capabilities.

What is an agent's memory?

It may refer to preserving part of the conversation, storing relevant preferences, or retrieving information from documents and databases.

It is not advisable to interpret "has memory" as if it infallibly remembered everything that happened. The system can lose context, retrieve an outdated document, or provide an incorrect explanation. Important data needs a verifiable source.

What can go wrong?

An agent can misinterpret an instruction, repeat an operation, use outdated information, or choose the wrong tool.

There is also the risk of prompt injection: external content that attempts to trick the model into ignoring instructions and executing unauthorized actions. For this reason, untrusted data must be separated from privileged instructions and permissions must be limited per tool.

A system that manages infrastructure should log actions, require confirmation for critical operations, and be able to recover state if something fails.

When is it convenient to use agents?

They make sense for tasks that mix reading, interpretation, and the use of multiple tools. If you only need to transform a CSV file following exact rules, a script can be cheaper and more reliable.

The right question is not "how do I add AI to everything?", but "what part of the work requires flexibility and what part is better left completely defined?".

Agents and ArduMaker

ArduMaker offers accessible infrastructure tools through its MCP integration. An agent with the appropriate permissions can request administrative operations without the person having to write each command manually. This does not eliminate the need to verify results or control which actions are authorized.

In summary: a chatbot converses; an agent can coordinate steps and tools to achieve a goal. The real difference lies in the authorized capabilities and system controls.

Sources: OpenAI Agents Guide · Agent Tools · What is MCP in ArduMaker.