Artificial Intelligence Agents: What They Are and How They Work

Artificial Intelligence Agents: A Simple Explanation

Quick answer: Artificial intelligence agents are systems capable of receiving a task, organizing several steps, using tools, and acting with a certain degree of autonomy. Unlike a chatbot, which only responds, an agent can look up information or perform tasks. This capability is useful, but it requires limited permissions, human oversight, and verification of results.

The artificial intelligence agents have become one of the most frequently used terms in the tech industry. The term may sound as if a machine were thinking and working on its own, but the reality is more specific: these are programs that combine an AI model with instructions, temporary memory, and access to certain tools.

The U.S. National Institute of Standards and Technology (NIST) describes these systems as capable of planning and taking actions that affect real programs or environments. This definition allows us to distinguish between a written response and an action: writing an email is one thing; accessing an application and preparing it within that application is another.

What Sets an Agent Apart from a Chatbot

A traditional chatbot receives a question and generates a response. An agent can break down a task into steps, choose a tool, review the result, and move forward. For example, in response to the request “organize these documents,” the agent could review authorized files, classify them, and propose a structure.

What They Can Do Right Now

Agents can now assist with investigative tasks, information classification, scheduling, document analysis, and report preparation. They can also coordinate various tools within a defined workflow. Their performance depends on the quality of the model, the permissions granted, and the clarity of the objective.

Not all demonstrations result in reliable processes. A task that works once may fail when faced with a different file, an ambiguous instruction, or a change in the application. That is why it is important to evaluate the system using real-world scenarios, not just examples prepared for a presentation.

Why They Need Access and Permissions

To take action, an agent must connect to something: documents, calendars, browsers, knowledge bases, or programs. The more tools it has, the greater its capabilities—and the greater the impact of an error. The key question is not just “What can it do?” but “What can it access, and what can it modify?”.

NIST is studying identity and authorization mechanisms for these systems. The practical recommendation is to apply the principle of least privilege: grant access only to the data and actions necessary for the task, for as long as required.

Risks You Should Understand

• Malicious instructions: A document or web page may contain hidden commands that attempt to mislead the agent.
• Incorrect actions: The system may misinterpret the target and modify something it wasn't supposed to.
• Information Display: Permissions that are too broad may reveal data unrelated to the task.
• Overconfidence: An automated response may contain errors or unverified conclusions.
• Lack of traceability: If there is no clear record, it can be difficult to understand why a decision was made.

The NIST's official analysis of AI agents It includes risks such as indirect injection of instructions, altered models, and actions that deviate from the defined objective.

How to Test an Agent Carefully

1. Start with a reversible task that doesn't involve any sensitive data.
2. Define what it can read, what it can suggest, and what it cannot modify.
3. Requires human confirmation before taking important actions.
4. Keep a record of the tools used and the results.
5. Test incomplete, ambiguous, and adverse scenarios—not just the ideal scenario.
6. Compare the results with a manual process before expanding access.

A tool, not a freelancer

The word “agent” can create unrealistic expectations. These systems have no legal liability, no human judgment, and no complete understanding of context. They are tools capable of chaining together probabilistic decisions and programmed actions.

Its value becomes apparent when the objective is clearly defined, access is proportional, and a person retains the final decision-making authority. You can continue learning about models, automation, and their limitations in the Artificial Intelligence from UNEMPRENDE.