The term AI agent has become one of the most used and worst explained in the technology sector. It is used to describe everything from a basic chatbot with predefined responses to autonomous systems that can research, plan and execute complex tasks independently. This guide explains exactly what an AI agent is, how it works internally and when it makes sense to implement one in a business.
The distinction matters because AI investment decisions are being made with confused concepts. A company that deploys an FAQ chatbot thinking it has an AI agent is paying for one thing and expecting another. And a company that dismisses AI agents because “we already tried chatbots and they didn’t work” is missing something qualitatively different.
The fundamental difference: from models to agents
A language model (such as Claude or GPT-4) is a system that takes a text input and produces a text output. It is powerful for generating, summarising, translating and reasoning, but it cannot do anything in the world outside that conversation. It cannot search for up-to-date information, it cannot access your systems, it cannot execute actions.
An AI agent is a system that uses a language model as its reasoning engine but equips it with tools: it can search the internet, read and write files, call APIs, execute code, and interact with databases. The difference between a model and an agent is the difference between a brilliant consultant locked in a room with no phone and the same consultant with access to all the information and the ability to act on the world.
The basic architecture of an agent has three components: perception (the agent receives information from the environment), reasoning (the language model decides what to do with that information), and action (the agent carries out actions in the world using the available tools).
Types of AI agents by autonomy
Single-step agents
These are the simplest. They receive an instruction, reason about it, execute an action and deliver the result. They have no memory between interactions, they do not learn from previous sessions and they cannot make decisions across multiple steps.
Example: an agent that receives a customer email, classifies the intent, extracts the relevant data and puts it into the CRM. One step, one result.
Multi-step agents
They can break a complex task down into subtasks, execute them in sequence (or in parallel), and combine the results to produce the final output. They can adapt if one of the steps fails.
Example: an agent that receives the instruction "prepare a report on the mechanical workshops market in Bizkaia for a commercial proposal". The agent breaks down the task: it searches for sector data, analyses competitors in the area, checks the CRM to see whether there are already clients in that sector, extracts the relevant information from the company’s knowledge base and generates the structured report with all that context.
Agents with memory
They maintain context between sessions. They remember user preferences, interaction history, previously taken decisions and the status of ongoing tasks. They have the greatest impact on user experience because the conversation does not start from scratch each time.
Our conversational Chatbots and Voice Agents operate with this architecture.
Multi-agent systems
Multiple specialised agents that collaborate to complete complex tasks. There is an orchestrator agent that coordinates, specialised agents that execute specific parts of the task, and a communication system between them.
This is the most powerful model and the one that scales best for complex business processes, but also the most costly to design and maintain correctly.
How an agent works inside: the reasoning-action loop
The central mechanism of most modern agents is called ReAct (Reasoning + Acting). The loop works like this:
1.The agent receives the task or observation from the environment 2. Reasons: "To complete this task I need to do X, Y and Z. I’ll start with X." 3. Acts: executes action X using the corresponding tool 4. Observes: receives the result of the action 5. Reasons again: "X has given me result A. Now I need to do Y taking A into account." 6.Repeats until the task is complete or a step limit is reached
This loop is what allows agents to handle tasks they were not explicitly programmed for: they can improvise, adapt to unexpected results and find alternative paths when the first one does not work.
Business use cases with the greatest impact
Commercial prospecting agent
It receives a sector and a geographical area, searches for companies that match the ideal client profile, analyses their digital presence, generates a personalised email for each one addressing their specific problems and sends it in a controlled manner. Without any member of the team having been involved in a single step.
It’s exactly what our Prospector + Motor de Outreach does. A well-configured agent can process between fifty and eighty prospects a day with a level of personalisation that previously required manual work from a full-time sales rep.
Customer service agent
It reads the incoming ticket or message, understands the user’s intent, consults the company’s knowledge base and the customer’s history in the CRM, generates a personalised response in the brand’s tone and sends it. If the query exceeds its resolution capacity, it hands off to a human with the full context of the conversation.
The automated Email Support implements this pattern with automatic resolution rates of 70–80% in typical cases.
Proposal generation agent
It receives a new client brief, consults the company’s service catalogue, searches similar cases in the project history, generates the structured commercial proposal, formats it and sends it to the CRM for review before dispatch. What used to take an account manager two hours, the agent does in five minutes.
Reporting agent
Every week or every month, the agent collects data from all relevant sources (Google Analytics, Meta Ads, CRM), generates the comparative analysis with the previous period, identifies the most significant variations, drafts the executive summary in natural language and sends the report by email to the configured recipient. Our Automatic Reports product works exactly like this.
SEO agent
It monitors target keyword positions, detects relevant ranking changes, identifies new keyword opportunities, generates content briefs optimised for the detected opportunities, and creates the monthly action plan. We explore this in detail in our article on SEO agents.
The real limits of AI agents in 2025
Agents fail more on long, complex tasks. The more steps a task has, the more opportunities there are for the agent to make a mistake or take a suboptimal decision. Current agents are far more reliable on ten-step tasks than on hundred-step ones.
Agents hallucinate. The language models that power them can generate incorrect information, especially in highly specific domains or with very recent data. In applications where the stakes are high, there must always be a human validation step before acting on the agent’s output.
Agents need well-designed tools. An agent is only as good as the tools available to it. If the APIs it can use have an inconsistent interface, if the knowledge base it consults is out of date, or if the instructions for using the tools are ambiguous, the agent will make mistakes.
The computational cost is real. Multi-step agents make multiple calls to language models. For high-frequency tasks, the cost can be significant. Optimising the number of steps and choosing the right model for each task are important architectural decisions.
How to assess whether your company is ready for AI agents
Three questions to assess maturity:
1. Do you have well-documented processes? An agent cannot automate what has not been defined. If the process “depends” on the experience of whoever carries it out, it must be documented first.
2. Do you have structured, accessible data? Agents need access to your company’s information: the catalogue, the CRM, the customer history. If that information is in scattered PDFs and in people’s heads, the agent cannot consult it.
3. Do you have the capacity to review and correct outputs? The first months of operation of any agent require supervision. Someone has to review the outputs, identify errors and update the instructions. Without that maintenance capacity, the agent will degrade.
If all three answers are yes, AI agents can generate immediate value. If any is no, the prior work in that area is a prerequisite for a successful implementation.