AI agents: the next leap for businesses (and what changes in 2026)

A year ago, the question was "should I use ChatGPT in my company?". Today, the question has changed. And anyone still stuck on the old one is already behind.

The news isn't AI that writes emails or summarises documents. That's old hat by now. The news is AI agents, systems that don't just answer when you ask, but carry out tasks from start to finish, on their own, and let you know when it's done.

Gartner predicts that by the end of 2026, 40% of enterprise applications will have AI agents built in, up from less than 5% in 2025. This isn't an optimistic software-salesman forecast. It's the direction every major vendor is pushing in. And, as always, Portuguese SMEs will have two options: ride the wave or get hit by it.

What's the difference between a chatbot and an agent?

This distinction is the most important part of the article, so it's worth pausing here.

A chatbot (or an assistant like ChatGPT) is reactive. You ask, it answers. You request a commercial proposal, it writes a draft. You ask for a summary, it summarises. But for every step, you're the one at the wheel. You copy, you paste, you decide the next step, you open the next app.

An AI agent is proactive and autonomous. You give it a goal, "handle this supplier's invoice", and it decides the steps on its own: it reads the PDF, extracts the figures, checks them against the order, records them in the accounting system, and flags them for payment. Without you touching anything between the request and the result.

The practical difference is this: a chatbot saves you minutes on a task. An agent takes the whole task off your hands.

What an AI agent can actually do in a real business

Let's leave the theory behind. Here's what's already possible today, without multinational budgets:

Handling requests end to end. A customer emails asking for a quote. The agent reads the email, identifies the products, looks up the current prices, generates the proposal, and leaves it ready for you to review and send. What used to take 40 minutes now takes the time of a quick read.

Scheduling and sales follow-up. The agent tracks the contacts left hanging, spots who hasn't replied in two weeks, drafts a follow-up email tailored to each case and schedules the send. What normally falls through the cracks starts happening on its own.

Reconciliation and admin tasks. Matching invoices against orders, categorising expenses, spotting figures that don't add up, preparing reports for the accountant. Tasks that eat up hours every week and that nobody enjoys doing.

Customer support that resolves, not just replies. The previous generation of chatbots answered questions. An agent can, within limits you define, change a delivery address, reschedule an order or open a return request, real actions, not just text.

Monitoring and smart alerts. The agent watches your data, sales, stock, indicators, and warns you when something needs attention, in plain language, before the problem grows.

The big shift of 2026: several agents working as a team

Here's the part that separates the hype from the reality this year. Until recently, we had one agent trying to do everything, and failing at the more complex tasks. The big 2026 trend is multi-agent systems: several specialised agents collaborating, each doing what it does best, coordinated by a central one.

Think of it as a team rather than a jack-of-all-trades. One agent handles research, another the writing, another the checking, another the execution. Just like in a real company, specialisation produces better results than asking one person to do everything.

For an SME, this means tasks that used to be too complex to automate, "analyse these 200 contracts and tell me which ones have auto-renewal clauses this quarter", are now within reach.

The risks nobody tells you about in the demo

Now the honest part. Giving an AI system autonomy is powerful, but it's precisely because it's autonomous that it needs more care, not less. And the numbers back this up: Gartner estimates that over 40% of agentic AI projects will be abandoned by 2027, mostly because companies underestimate the costs, the security and the organisational change they require.

An agent that errs, errs at scale. A chatbot gives you a wrong answer and you correct it. An agent acting wrongly can send 200 wrong emails, change real records or approve what it shouldn't, before you notice. Autonomy amplifies both the wins and the mistakes.

The hidden costs. Every action an agent takes consumes resources. A poorly designed agent that loops or repeats tasks can generate surprise bills. You need to design limits and brakes.

Permissions, for real. An agent with access to your email, CRM and invoicing system is, in practice, an employee with access to everything. It must have the minimum permissions necessary and nothing more. This is a security matter, not a technical detail.

GDPR still applies. If the agent processes customer data, all data-protection rules still hold, made worse by the fact that the system acts on its own. You need to know what it touches and why.

How to start without getting burned

You don't need to transform the whole company. You need to pick the first case well.

Pick a boring, repetitive, low-risk task. The ideal candidate is something done often, that always follows the same pattern and where a mistake isn't catastrophic. Email triage, document organisation, first drafts. Avoid starting with something that touches money or customers.

Keep the human in the loop. In the first phase, the agent proposes and you (or your team) approve. This gives you confidence and shows you where it goes wrong before you hand over the keys.

Measure before and after. How long did the task take? How long now? How many errors? Without numbers, it's just a feeling.

Only then expand. When one case works and you trust it, move to the next. That's how you build it, case by case, not with a giant project that promises everything and delivers frustration.

Where this stops being DIY

Off-the-shelf tools get you to your first simple agent. But when you want an agent to talk to your ERP, your CRM and your invoicing system, at the same time and securely, you enter another level. When you need to set real permissions, log everything the agent does so you can audit it, and ensure GDPR compliance, it's no longer a matter of subscribing to an app.

That's where a partner like Sparksoft comes in. We don't sell you "an AI agent" like a catalogue product. We look at your processes, identify where autonomy gives you real returns and where it gives you problems, and build something that works with the systems you already have, with brakes in the right places.

The difference between an agent that saves you hours and one that creates a new problem is almost entirely in the design. And the design is the part the demo doesn't show.

The window is now, but haste is a poor adviser

AI agents are probably the biggest change in how we work since email arrived in companies. Two years from now they'll be normal. The companies that learn to use them well, with judgement, will have teams doing the work that matters while agents handle the rest.

But "now" doesn't mean "in a rush". The difference between those who win with agents and those who join the 40% that give up isn't speed, it's judgement. Start small, measure, and expand with a clear head.

Portugal has talent and companies with drive. What's often missing is someone who separates what's real from what's hype, and who helps take the first step without falling into the traps.

Consider this article that warning, and that invitation.