What are AI agents and how do they help your company?.

AI agents are a leap into a system that responds to another system that acts, decides, and solves on its own, without anyone having to give it instructions at every step.

Imagine delegating to an indefatigable colleague who never sleeps, never gets bored with repetitive tasks, and learns from every interaction to do it better the next time. That is, in essence, what changes with a AI agent: the automation It stops being synonymous with rigidity; tasks are executed from start to finish on their own, and decisions that used to take hours are made in seconds, with real data in front of you and not with assumptions. Continue reading and you’ll see what it is and how it works. 

When we talk about what it is an AI agent We are talking about a cycle that repeats itself without stopping; it perceives, decides, acts, and learns. That is how it works, step by step.

Perceives and collects data 

The agent gathers the context they need to make a meaningful decision.

It all starts with the information that the agent has at hand. This can come from a conversation with the user, from an internal system of the company (a CRM, an ERP, a booking database), or from real-time changing data, such as the stock in a warehouse or the status of an order. 

Think and decide 

With that information on the table, the agent evaluates what options are available and which one best fits the goal that has been set for it. It is not an automatic response or a rigid decision tree; it is a process that weighs alternatives, as a person would do with the same information in front of them, and chooses the action that most closely approaches solving the user’s actual problem. 

Executes the action 

Here is the difference with a chatbot in use. A chatbot responds. An agent acts: it queries a system to check the availability of an appointment, completes a management task in the CRM, launches a billing process, or reserves a resource without anyone having to do it manually. The on-screen response is only the visible part; behind it lies a real task that has been executed.

Learn and improve 

The cycle doesn’t end when the action is executed. Every interaction leaves information about what worked and what didn’t, and the agent incorporates that information for the next time. It’s not a system that stays the same from day one: it improves with use, and that means that its behavior today won’t be the same in a few months. That’s exactly what the evolution of AI agents is about, which we’ll see below. 

To understand what an AI agent is and its relevance, we must know the reasons behind its evolution.

The first conversational systems that arrived in companies were chatbots based on certain patterns; if the user typed «schedule,» the system responded with a preset text about schedules. Outside of that script, there was nothing. Any question that didn’t fit into the decision tree ended in a «I didn’t understand your query» or in the hands of a person. In other words, they only work within a very narrow scope.

What changed the evolution of AI agents was the arrival of language models (LLMs). For the first time, a system could understand a written request naturally, without needing to match it with a pre-programmed sentence, and connect that understanding with the ability to act on real systems: querying a database, updating an order, escalating an incident. At that moment, it was when the technology stopped being a lab demo and started solving real business problems.

AI agent vs. chatbot vs. virtual assistant

The question is not which one is more advanced, but how much autonomy each has and what they can do without a human giving the order at every step.

A chatbot follows a closed script. It does not decide anything on its own and does not execute anything outside of the actual conversation: if the question does not fit the programmed scenario, it remains without any useful response. virtual assistant, A type of Siri or Alexa, it goes one step further because it does execute actions, but only when the user explicitly asks for them one by one: it has no self-initiative; it waits for orders one by one. An AI agent breaks that dependence. It acts autonomously towards a goal, connects with several systems at the same time, and chains together the necessary steps to complete it, without anyone having to give it instructions at every step of the way.

Component	What he does	In practice	What you need to know
Language model (LLM)	Read what the customer writes and understand what they want. Then decide how to respond.	«I want to change my order» and «I made a mistake in the size» lead to the same management. The agent recognizes the intention behind each phrase.	It is the most visible piece, but rarely the one that makes the difference. What distinguishes an agent is what is built around it.
Memory	In the short term, the conversation continues. In the long term, it keeps the useful information from each client.	If the customer types «the 4521», the agent knows that it’s their order. Months later, they remember what format they want the invoices in.	Choosing what to remember is a business and data protection decision. Saving more complicates GDPR compliance.
Tools and connections	They connect the agent with the CRM, ERP, payments, or calendar. They usually work through APIs.	Check the status of the order in the ERP. Then, launch the return without anyone from the team needing to intervene.	He can only do what his integrations allow. And it's not the same thing to check an order than to authorize a refund.
Reasoning and planning	Break each task down into steps and arrange them. If one fails, redo the plan.	In a return, check the order, review the conditions, initiate the process, and confirm. If the deadline has expired, change course.	A good agent also knows when to stop. Deciding which cases to take on matters as much as deciding which ones to solve alone.

Not all AI agents do the same thing, and classifying them by business area helps understand where each one fits in. These are the most common types of AI agents in a business environment.

Customer service agents

These Smart agents They manage inquiries, incidents, and repetitive tasks without requiring a person to intervene in each one. They don’t just answer common questions; they check the actual status of an order, handle a return, or escalate the case. For example, a customer writes that their order hasn’t arrived; the agent then checks the logistics system, checks that there’s a carrier delay, informs the customer with the estimated date, and, if the customer requests it, processes a customized response.

Sales and marketing agents

The New technologies in marketing They also work with these agents to accompany potential customers from the initial contact to conversion. They adapt the message based on how the customer responds and customize their offers. In addition, they qualify leads, resolve pricing or feature questions, and know when to pass the conversation to a member of the sales team. If a user visits a website and asks about the differences between two subscription plans, the agent identifies what need they have based on their responses and recommends the appropriate plan. 

Agents of operations and internal processes

Here, the agent does not talk to an end customer; instead, they solve internal tasks that previously required someone to do them manually, reviewing several systems at the same time. They coordinate inventory, generate reports, or trigger alerts when something goes wrong. The agents AI in retail They also automatically check or generate a replacement order or a product with a minimum stock. 

To understand how to create an AI agent, it is worth taking a look at the entire process. It all starts with defining the goal and the use case: it is not the same thing to have an agent for returns as it is to qualify leads; that decision determines what data to use and which language model best fits that task.

Then comes the knowledge and role part. Train the agent with the company’s actual data and processes, connect them to the systems where they have to act (CRM, ERP, reservations…) and, before launching it, thoroughly test it and deploy it with close monitoring in the first few weeks. 

Each phase has room for error if it is not done with discretion, and that is where the difference lies in having a team that has already traveled this path before. If you want Create AI Agents for your company, in Imascono we help you build it from start to finish. 

The benefits of bringing an AI agent into a company’s day-to-day operations are immediate: saving time on repetitive tasks, 24/7 availability, scalability without the need to hire more staff, and better decision-making by freeing up the human team to focus on what truly requires judgment.

But no benefit can compensate for poorly planned implementation. One of the most common fears is what happens to the company's data. At Imascono, we work hard to ensure that our data is secure. Artificial Intelligence services from the security and protection of data in each project, accompanying you throughout the entire process, so that the implementation is a controlled and efficient process.

Do you want to know which AI agent would fit your company? In Imascono We help you define it, from consulting to implementation. Contact us and we take the first step together.