It seems like everything is an AI agent now. The chat that answers questions on the website? An agent. The assistant that consults company documents? Also. The system that connects with the CRM and manages a request? An agent, obviously. At this rate, any day now, the good old contact form will ask us for a job title update.
Behind this word are solutions with very different capabilities, limits, and degrees of autonomy. Some inform, others propose, and still others can execute actions within business systems. The boundaries blur, and labels don’t always help us understand what we are actually acquiring.
At DMS3consulting, when we design and implement these solutions, the questions that interest us are specific: what task do they need to solve, with what information, to which systems do they need to connect, and how far will we let them act? This is where a good part of the result is determined.
That’s why we’ve prepared this survival guide: to orient you amidst promises, help you understand what each solution can bring to your company, and provide criteria when someone proposes incorporating an ‘agent’. It’s good for the name to grab attention. It’s much better if it solves a real need.
What is an Artificial Intelligence Agent and Why Does It Generate So Much Confusion?
In the context of generative AI, a practical definition is this:
an AI agent is a system that can decide which steps and tools to use to advance towards a goal, within established limits.
It can consult information, interpret the result, and decide what it needs to do next.
The problem arises when we try to turn this definition into an exact boundary. The term is also used to describe specialized assistants and more structured processes. A useful distinction is to observe whether the system follows predefined steps or if it has room to decide how to proceed based on what it encounters.
Hence our ‘but it is’. An assistant can incorporate agent capabilities without becoming that digital worker who, according to some demos, will manage your company while you have coffee. To evaluate it, it’s worth asking what it can do, with what data, and under what supervision.
What is the Difference Between a Chatbot, an AI Assistant, and an Agent?
These names describe different aspects that can be combined. ‘Chatbot’ primarily refers to a way of interacting: a conversation. ‘Specialized assistant’ emphasizes function and context. ‘Agent’ points to the system’s leeway in deciding how it performs the task.
Concept | What it helps us understand | Business Example |
|---|---|---|
Chatbot | Interaction is done through conversation. Capabilities depend on the underlying system. | A website chat that answers visitor queries. |
Specialized Assistant | Works with instructions and information from a specific domain, and can incorporate tools. | An assistant that consults manuals and provides guidance on company products. |
AI Agent | Has leeway to select steps and tools based on what it encounters. | A system that investigates an incident by consulting the order and shipping, and prepares a resolution proposal. |
Therefore, an agent can function within a chat, and a specialized assistant can have agent capabilities. It doesn’t necessarily need to be able to modify data: investigating an issue with various tools may already require decisions on how to proceed. The interface, available knowledge, and autonomy are different dimensions.
AI Agents and Automation: When Does the System Need to Make Decisions?
An automation can follow an established circuit: a form comes in, a contact is created in the CRM, and a task is assigned to the corresponding salesperson. It can include conditions, checks, and error management. It doesn’t need to be ‘intelligent’ to be very useful.
The difference appears when the system has room to decide its path. Imagine a review of industry news: it can limit itself to consulting a few sources and preparing a summary, or it can evaluate the results, detect missing information, and perform a new search to complete it.
The same applies to a website review. We can check a fixed list of links or instruct a system to explore pages, investigate incidents it finds, and collect evidence. In this second case, the scope of the review, permitted checks, and when the task should be considered finished must be defined.
Whether a task starts with your request or every Monday at nine doesn’t determine if it’s an agent. What matters is how it’s executed. In a project, we can combine fixed steps with AI decisions and human validation points.
What are AI Agents Used for in a Company?
To understand their potential, it helps more to look at a specific need than to imagine an AI capable of doing everything. Here are some applications where assistants, automations, and agent capabilities can work together.
Customer Service: Useful Responses with Business Information
An AI assistant can help customers find the right product, consult a manual, or know where to go to resolve an incident. To be useful, it must work with reliable company information and follow the criteria defined by the company.
The value lies in providing an appropriate answer to each query and escalating those that require personalized attention to the team. All while maintaining control over what is communicated to customers.
Sales Support: Better Preparing Each Opportunity
Consider a B2B company that receives quote requests. A solution could interpret the query, retrieve the available history from the CRM, identify missing information, and prepare a summary for the salesperson, along with a draft response. The team would receive a more refined request and could dedicate more time to evaluating it.
Incident Management: Gathering Information and Proposing a Resolution
In e-commerce, investigating a delay may require consulting the store, the ERP, and the carrier. A solution connected to these services could gather the information, point out discrepancies, and prepare the response. Depending on the agreed permissions, it could also open an incident or leave an action pending approval.
The examples of sales support and incident management are possible applications, not functionalities that every tool incorporates by default. Their viability depends on the available systems, access to data, and the process to be solved.
How to Know Which Artificial Intelligence Solution Your Company Needs
A good way to start is to put on the table a task that the team repeats often and that consumes time. For example: answering the same queries, searching for information in various applications, or preparing documentation before making a decision.
From there, it’s worth evaluating three questions: how often it happens, how much each case varies, and what the cost of making a mistake is. A frequent query with clear documentation might fit a specialized assistant. A data transfer with stable rules can be solved with automation. An investigation that changes based on the data found may justify more room for decision-making.
We also need to know where we want the system’s work to end. Preparing a purchase proposal and sending it to the supplier have different implications. We can gain a lot of value by having the proposal ready and reserving approval for the responsible person.
As implementers, this is one of the first points we work on with the company: specifying a useful outcome, a manageable scope, and a way to check if the solution works. The amount of autonomy must respond to this need.
What Should You Ask Before Hiring an AI Agent?
A demo can be spectacular. To evaluate a proposal, however, you need to understand what will happen when your data, your customers, and your daily exceptions are introduced. These questions help to clarify it:
- What exactly will it do? The tasks included, the expected outcome, and the cases it will need to escalate to a person must be defined.
- What information will it work with? It’s important to know where the answers will come from, how data will be updated, and what will happen if it’s incomplete or contradictory.
- To which systems will it connect? Consulting an order, modifying it, and processing a return require different accesses and functions. The proposal must clarify what each integration includes.
- What can it decide without approval? Permissions must be clearly defined, especially when there are changes to data, communications to customers, or financial operations.
- What will it do when it makes a mistake or cannot proceed? There must be a way to detect incidents, review what happened, and ensure service continuity.
- Who will maintain it and how will we evaluate the results? It is necessary to foresee responsibilities, operating costs, and indicators such as time saved, quality of responses, or correctly completed tasks.
The answers should allow comparing proposals beyond just the platform’s name. ‘It has AI’ is too short a description to decide on an investment.
Implementing AI in the Company: Data, Integrations, and Professional Judgment
Between a demonstration and a tool that the team can use every day, there is work of analysis, integration, and validation. It is necessary to understand the process, prepare the information, connect the services, and check behavior with real cases, including those where data is missing or something goes wrong.
If the assistant needs to respond about availability, it will require access to a sufficiently updated stock source. If it needs to consult commercial conditions, it will be necessary to identify which ones are current. And if it needs to modify an order, the system will have to verify that it has permission to do so. A well-written answer alone does not solve any of these issues.
At DMS3consulting, we approach these projects from business process to implementation: we define what the solution needs to solve, we work on connections with the website, e-commerce, CRM, or ERP, and we establish how to test it and review its operation. This allows us to put technology at the service of a specific need and adjust it as the business evolves.
Perhaps we will end up talking about an assistant. Perhaps about an automation. Perhaps about an agent, with all its nuances. What must be clear is what task it solves and under what conditions you can trust it.
