Imagine that at 2:13 in the morning an employee clicks on a phishing link. While the IT team sleeps, a system detects a suspicious login, blocks the affected account, checks whether other devices are compromised, opens an incident and drafts a summary for the security officer. That is the kind of work an AI agent can do today. And the good news is that understanding how to build an AI agent is no longer just a matter for programmers: anyone in your company with a clear idea can build a first prototype. In this article we explain the path, with our feet on the ground and thinking about your business.
What an AI agent is (and what it is not)
It is worth starting with the key difference. An AI chat application answers questions: you ask it something and it replies. An AI agent goes a step further: in addition to answering, it executes multi-step tasks using tools and data. It can read an inbox, classify messages, prepare a draft report or open an incident without anyone guiding it through every action.
A useful way to see it: if chat models are like conversational search engines, agents are like applications for a world with AI. They do not replace people; they take repetitive work off their shoulders so they can devote their time to what adds value.
In an SME this translates into very recognizable things: tracking project deadlines, monitoring a shared mailbox, generating the nightly sales report or preparing the first draft of a weekly report from emails and messages.
How to build an AI agent step by step
The process is simpler than it seems. These are the steps we recommend following, inspired by the guide published by Microsoft on its Signal blog («How to build an AI agent: A simple guide for anyone», August 2026).
1. Start with the problem, not the technology
The right question is not «what can AI do?», but «what problem do I want to solve?». Talk to the team that suffers the task, define the scope well and set the expected outcome: do you want the agent to retrieve information, to complete a task or to act autonomously?
A typical example: a team spends hours every week putting together a status report from emails, messages and documents. It is repetitive, details slip through and the result changes depending on who writes it. That is a perfect candidate for an agent.
2. Check what already exists
Before building anything, look to see whether the work is already solved. Often a model or a pre-built agent covers much of the case. If you do not find exactly what you are looking for, assess your technical level: to start without writing code, tools like Microsoft 365 Copilot are designed so that anyone can get up and running quickly; more advanced developments do require developer tools to gain control and customization.
3. Build a first version
Building starts by describing in natural language what you want the agent to do. From there you define its behavior: tone, tasks and limits. This is where you shape how it responds, so it is consistent and fits your real workflow.
Going back to the shared mailbox: you could tell it to classify messages into «general queries», «urgent matters» and «complex cases», to respond with approved language to routine questions and to refer anything sensitive to a person.
4. Give it context and define what it produces
An agent performs to the extent that you give it good information. Connect it to the right sources —emails, documents, a shared repository or a website— and decide whether it should stick only to your curated data or can consult broader sources.
Then define the output format: a report, a spreadsheet, an email reply? In the weekly report example, you could ask it to «review the messages and emails from the last seven days», to «focus only on a specific project» and to identify decisions, blockers and upcoming deadlines, with a fixed structure and length. One important detail: tell it to use only that information and not to invent missing data. It is the best defense against «hallucinations».
5. Test, share and scale
Put it to work in real scenarios and adjust it. You may discover that the report is too long, or that the agent receives contradictory information and it is better to ask it to flag it rather than guess. The goal is not perfection from minute one, but having something useful and improving it over time. When it works, you will be able to expand its capabilities or take it to more advanced tools.
From prototype to business: where Tisa comes in
Building a simple agent is achievable; turning it into a reliable piece of your operations is another story. That is where judgment makes the difference. At Tisa we have been helping private companies take advantage of technology without chasing fads since 1987, and with AI we apply the same rule: a measurable use case before "AI for the sake of AI".
Our approach starts with your data, because without organized information and good data governance, no agent performs. As a Microsoft partner and certified ISV in Business Central and Power Platform, we help you to:
- Identify which repetitive process is worth automating and with what return.
- Define data governance and the sources the agent can use.
- Land the use case on your ecosystem (Microsoft 365, Power Platform, Business Central).
- Train your team so it gains autonomy.
We do not promise magic: we propose starting small, measuring and scaling what works.
Take the first step
If you have in mind a task that consumes hours every week, you probably already have your first use case. Do you want to know if it is viable in your company? Write to us at info@grupotisa.com or call us at (+34) 971 305 885 and we will give you a no-obligation assessment. At grupotisa.com you will find more about our AI and data line.
Reference source: Microsoft Signal, «How to build an AI agent: A simple guide for anyone» (10 August 2026).