An AI agent talks with your customers on WhatsApp, checks your systems, gets the task done and hands off to a person when a case needs a human decision. It works 24 hours a day, and every action is logged.
Illustrative example
An AI agent is software that takes a goal (qualifying a lead, handling a product exchange, reconciling an invoice), works out the steps on its own, carries them out in the company’s systems and hands back the result. A chatbot only talks; an agent talks and acts.
It reads free-form text, voice messages or photos and figures out what the customer wants, even when they don’t explain it clearly.
It looks up the order, generates the shipping label, updates the CRM, schedules the visit. It doesn’t just suggest: it does the work and logs what it did.
It knows what was discussed last time and what has already been tried. It answers consistently, without contradicting itself.
All three coexist. Using an agent where a chatbot would do means spending money you don’t need to; using a chatbot where the case calls for judgment gives the customer a poor experience.
| This pageAI agent | Traditional chatbot | Classic automation | |
|---|---|---|---|
| What it does | Understands the goal, decides the steps and carries them out | Follows a script of questions and answers | Runs a fixed sequence of steps between systems |
| Best for | Customer service with many variations, sales and triage that requires judgment | Frequently asked questions with a standard answer | Moving data between stable systems on a set schedule |
| When something goes off script | Asks, tries another route or hands off to a person | Gets stuck and calls in a support rep | Stops and alerts someone |
| Maintenance | Medium: ongoing tuning based on what production reveals | Low, but the script gets outdated fast | Low, but it breaks when a screen or format changes |
When the work involves no conversation, just steps between systems, the answer is usually process automation.
The most common use cases at companies already running agents in production.
Three signs point to an agent. If they’re not there, a simple workflow or a chatbot does the job better and for less.
Deploying an agent methodically is different from switching on a chatbot and seeing what happens. Each stage moves forward only once quality has been measured.
The current process, the decision points, the systems the agent will act in and how success will be measured.
You getThe agent spec, in one or two pages.
A lean version in a controlled environment, with a person checking the answers.
You getProof that it works, before you invest more.
First during business hours with supervision, then 24 hours a day, then with more autonomy.
You getThe agent serving real customers.
Broader scope, new systems and fine-tuning of cost and speed based on what production reveals.
You getResolution, time and satisfaction metrics.
A poorly deployed agent makes mistakes with real consequences or exposes data it shouldn’t. Every project addresses both risks.
Every piece of personal data sent to an AI model has a defined legal basis. Whenever possible, data is anonymized before it’s sent, and execution logs don’t store personal data.
Payments, external emails and important record changes go through human approval at first. Autonomy grows as the agent builds a track record.
Each interaction records what came in, what the agent decided, what it did and the outcome. Your team can audit it without depending on Sincron.
We know one of these use cases from both sides. Besides client projects, we run a WhatsApp service that answers questions and books appointments on its own, sold on subscription to independent professionals and clinics. Keeping an agent in front of real customers every day teaches you where it gets things wrong, what needs to go to a person and how much the results depend on the information being set up properly. Those lessons feed back into every project, as do the ones from the rest of Sincron’s own product catalog.
“Our contacts actually went up this week. And daily spend didn’t even reach half of what it used to be.”
It’s software that takes a goal (qualify this lead, handle this exchange, reconcile this invoice), works out the steps on its own, carries out the actions in your systems and returns the result. Unlike a chatbot, which only talks, an agent acts. Unlike traditional automation, which follows a fixed script, an agent adapts.
A chatbot follows a script of questions and answers and calls in a support rep when it gets stuck. An AI agent understands the goal in natural language, decides the steps, carries them out in your systems and adapts to variations, using AI for the decisions that require judgment.
For well-defined, monitored tasks, yes. The rule is to start with low-risk cases (qualification, categorization, triage), keep a person approving decisions that can’t be undone and log every run so it can be audited and fine-tuned.
There are two costs: development (mapping the process, connecting the systems, testing and validating) and operation (AI model usage and infrastructure). Operating costs have dropped sharply in recent years; mid-volume agents cost tens to hundreds of reais (R$) a month to run. The proposal includes both figures before any work begins.
Three layers: a well-defined scope (the agent only carries out approved actions), a rule before any action that can’t be undone (human approval above a certain amount, for example) and monitoring, with the agent’s reasoning logged and automatic handoff to a person in ambiguous cases.
Yes. The agent answers through your company’s WhatsApp number, as well as on your website and by email. When a case needs a person, the conversation passes to a team member with the full history, so the customer doesn’t have to repeat anything.
Usually 2 to 4 weeks. A proof of value is deliberately smaller than the final version: it runs in a controlled environment, with a person checking the answers. The goal is to prove that it works, or find out early that it doesn’t, before investing at scale.
The free assessment shows which processes call for an agent, which can be handled with simple automation and which aren’t worth the effort.
